{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5885695856409807\n",
      "0.6528930147774489\n",
      "0.5058397918952761\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print(np.random.randn())\n",
    "print(np.random.randn())\n",
    "print(np.random.randn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.47595083  1.01090874  1.35920097 -1.70208997 -1.38033223]\n",
      "[2.10177668 0.42589917 0.12920023 0.56296251 1.09676472]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.randn(5))\n",
    "print(np.random.randn(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.80081885 -0.22308327]\n",
      " [ 2.06367066  0.0126235 ]\n",
      " [-0.8747738  -0.55707938]]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.randn(3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.44122748688504143\n",
      "-0.33087015189408764\n",
      "0.44122748688504143\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(5)\n",
    "print(np.random.randn())\n",
    "print(np.random.randn())\n",
    "np.random.seed(5)\n",
    "print(np.random.randn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09120471661981977\n",
      "1.331586504129518\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(8)\n",
    "print(np.random.randn())\n",
    "np.random.seed(10)\n",
    "print(np.random.randn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(100,) <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "x_data = np.linspace(-1,1,100)\n",
    "print(type(x_data))\n",
    "print(x_data.shape,type(x_data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.71527897, -1.54540029, -0.00838385,  0.62133597, -0.72008556,\n",
       "        0.26551159,  0.10854853,  0.00429143, -0.17460021,  0.43302619,\n",
       "        1.20303737, -0.96506567,  1.02827408,  0.22863013,  0.44513761,\n",
       "       -1.13660221,  0.13513688,  1.484537  , -1.07980489, -1.97772828,\n",
       "       -1.7433723 ,  0.26607016,  2.38496733,  1.12369125,  1.67262221,\n",
       "        0.09914922,  1.39799638, -0.27124799,  0.61320418, -0.26731719,\n",
       "       -0.54930901,  0.1327083 , -0.47614201,  1.30847308,  0.19501328,\n",
       "        0.40020999, -0.33763234,  1.25647226, -0.7319695 ,  0.66023155,\n",
       "       -0.35087189, -0.93943336, -0.48933722, -0.80459114, -0.21269764,\n",
       "       -0.33914025,  0.31216994,  0.56515267, -0.14742026, -0.02590534,\n",
       "        0.2890942 , -0.53987907,  0.70816002,  0.84222474,  0.2035808 ,\n",
       "        2.39470366,  0.91745894, -0.11227247, -0.36218045, -0.23218226,\n",
       "       -0.5017289 ,  1.12878515, -0.69781003, -0.08112218, -0.52929608,\n",
       "        1.04618286, -1.41855603, -0.36249918, -0.12190569,  0.31935642,\n",
       "        0.4609029 , -0.21578989,  0.98907246,  0.31475378,  2.46765106,\n",
       "       -1.50832149,  0.62060066, -1.04513254, -0.79800882,  1.98508459,\n",
       "        1.74481415, -1.85618548, -0.2227737 , -0.06584785, -2.13171211,\n",
       "       -0.04883051,  0.39334122,  0.21726515, -1.99439377,  1.10770823,\n",
       "        0.24454398, -0.06191203, -0.75389296,  0.71195902,  0.91826915,\n",
       "       -0.48209314,  0.08958761,  0.82699862, -1.95451212,  0.11747566])"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.90745689, -0.92290926,  0.46975143, -0.14436676, -0.40013835,\n",
       "       -0.29598385,  0.84820861,  0.70683045, -0.78726893,  0.29294072,\n",
       "       -0.47080725,  2.40432561, -0.73935674, -0.31282876, -0.34888192,\n",
       "       -0.43902624,  0.14110417,  0.27304932, -1.61857075, -0.57311336,\n",
       "       -1.32044755,  1.23620533,  2.46532508,  1.38323223,  0.34623312,\n",
       "        1.02251611,  0.16681027,  1.65671662,  0.66788961, -0.22994664,\n",
       "       -1.12955119, -0.6399626 ,  0.31383052, -1.22583598, -0.22179314,\n",
       "        1.33992631,  0.02930971,  1.98538575,  1.4471656 , -0.28762941,\n",
       "       -1.35931057, -0.04804133, -0.48078734,  0.37775309,  1.61440797,\n",
       "       -1.12310404, -0.38872795,  0.33234995,  1.13497317,  0.51071441,\n",
       "        0.41429764,  1.34454942,  0.49351532, -0.23700418,  0.05728515,\n",
       "       -0.70707145,  0.54666484,  0.94250041, -2.97959677,  1.21814885,\n",
       "       -0.05652072,  0.46088845,  0.66237401, -2.29510333, -1.19592931,\n",
       "       -0.33310116, -0.79139077,  0.27417278, -0.51490992, -1.7110712 ,\n",
       "        0.61229731,  1.10012937,  0.56435253, -0.71279944, -0.26085948,\n",
       "        0.54842807,  0.60319905,  1.00686114, -0.29442601, -1.42088052,\n",
       "       -0.67894677,  0.53388481,  0.7439744 ,  2.22504964,  0.11718142,\n",
       "        0.24461452, -0.17729882, -0.40572953,  0.78177519,  0.35347761,\n",
       "       -0.20727949, -1.07969738, -0.12306983, -0.39098219,  1.25517373,\n",
       "        0.94712608, -1.02231069,  1.16716837, -0.57197681,  0.1331375 ])"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(*x_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,) <class 'tuple'>\n",
      "(100,) <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "unpacked_shape = x_data.shape \n",
    "packed_shape = (*x_data.shape,)\n",
    "print(unpacked_shape,type(unpacked_shape))\n",
    "print(packed_shape,type(packed_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15],\n",
       "       [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n",
       "       [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],\n",
       "       [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]])"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_array = np.array([i for i in range(64)])\n",
    "int_array.reshape(8,8)\n",
    "int_array.reshape(4,16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<zip object at 0x0000013717951E40> \n",
      "\n",
      "第[1对 xy =(1.2027438739699048, 1.4465928263721342)\n",
      "第[2对 xy =(-1.0247529670452837, 1.0501186434681125)\n",
      "第[3对 xy =(0.16039916294255877, 0.025727891472673518)\n",
      "\n",
      "第1对 xs=1.2027438739699048,ys=1.4465928263721342\n",
      "第2对 xs=-1.0247529670452837,ys=1.0501186434681125\n",
      "第3对 xs=0.16039916294255877,ys=0.025727891472673518\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(10)\n",
    "y= x**2\n",
    "zip_data = zip(x,y)\n",
    "print(zip_data,'\\n')\n",
    "\n",
    "count = 0\n",
    "for xy in zip(x,y):\n",
    "    count += 1\n",
    "    if count <=3:\n",
    "        print(f\"第[{count}对 xy ={xy}\")\n",
    "print()\n",
    "count =0\n",
    "for xs,ys in zip(x,y):\n",
    "    count +=1\n",
    "    if count <=3:\n",
    "        print(f\"第{count}对 xs={xs},ys={ys}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "所有元素均值： 3.5\n",
      "0维度均值(所有行元素的均值): [2.5 3.5 4.5]\n",
      "1维度均值(所有列元素的均值): [2. 5.]\n"
     ]
    }
   ],
   "source": [
    "X = tf.constant([[1,2,3],\n",
    "                 [4,5,6]],dtype = tf.float32,name=None )\n",
    "y=tf.reduce_mean(X)\n",
    "y0=tf.reduce_mean(X,axis=0)\n",
    "y1=tf.reduce_mean(X,axis=1)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print('所有元素均值：',sess.run(y))\n",
    "    print('0维度均值(所有行元素的均值):',sess.run(y0))\n",
    "    print('1维度均值(所有列元素的均值):',sess.run(y1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0维度(行)均值,保持原维度:\n",
      " [[2.5 3.5 4.5]]\n",
      "1维度(行)均值,保持原维度:\n",
      " [[2.]\n",
      " [5.]]\n"
     ]
    }
   ],
   "source": [
    "y0_mean = tf.reduce_mean(X,axis=0,keep_dims=True)\n",
    "y1_mean = tf.reduce_mean(X,axis=1,keep_dims=True)\n",
    "with tf.Session() as sess:\n",
    "    print('0维度(行)均值,保持原维度:\\n',sess.run(y0_mean))\n",
    "    print('1维度(行)均值,保持原维度:\\n',sess.run(y1_mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "(3,) (2, 1)\n",
      "(1, 3) (2, 1)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "print(X.shape)\n",
    "print(y0.shape,y1_mean.shape)\n",
    "print(y0_mean.shape,y1_mean.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import  matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_data: [-1.         -0.97979798 -0.95959596 -0.93939394 -0.91919192]\n",
      "noise: [ 0.44122749 -0.33087015  2.43077119 -0.25209213  0.10960984]\n",
      "y_data: [-0.82350901 -1.09194402  0.05311656 -0.97962473 -0.7945399 ]\n"
     ]
    }
   ],
   "source": [
    "x_data = np.linspace (-1,1,100)\n",
    "print(\"x_data:\",x_data[0:5])\n",
    "\n",
    "np.random.seed(5)\n",
    "noise = np.random.randn(*x_data.shape)\n",
    "print(\"noise:\",noise[0:5])\n",
    "\n",
    "y_data = 2*x_data + 1.0 + noise * 0.4\n",
    "print(\"y_data:\",y_data[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ci1+Lu3kbwRpkuII5IkRE5BF6JsJqrvZZtUo2J1MLQm6+Gdi4EZg+XRGEWOiVR0NKDESIiMhj9LqBuxzk5J0H+vUDHnsMOHNG+UJDhsiqmbvvdvqeCQnAwYOyo2lKiv3Opn7VvM2ATEK42uHe+3JzcxEVFYWcnBxERkb6+nSIiMhFznabdZfaBnIxMf9L/Iz6Qe6Wq5bVWr06sGgR8OCDJT8JJ+fjr7sYe5KW+zcDESIi8iuKIKfpJYROGg/MmaP+hB49gA8/BCpX9uh52Ns11zJDE8xLOAxEiIgoOGzbBiQmAgcOKMeiooC5c2ULVnvJJW4ym2VHV3slxZbE3Kys4CjBtaXl/s0cESIi8j8FBcDUqUDr1upBSMeOsmlZ3766BCFz5nAXY09h+S4REfmX/fvlLMj27cqxsmVl6/Zhw2SJroep5YQ44smN7wIVAxEiIvIPhYVyKmL8eODKFeV48+ayOdntt+vy9vZyQhwJ5F1zPYVLM0REZHyHDwOdOskNZWyDkNBQYNo02b5dpyDE0V43aoJh11xP4YwIEQUtvUpMyYOEAD75BBg5EsjJUY7XqydnQZo10/U0nO11U1yw7JrrKQxEiCgosf+DHzh9Ghg6FFi9Wn181ChgxgyZF+IBjgJTLbkethvakWNcmiGioGNZ67f9DTc7Wz6emuqb86Jivv4aiItTD0JiYoAffpB3ew8FIampshw3Pl5W+8bHy+8tPwuu5nq8/bZ691Wyj31EiCiosP+DweXlyZ1yP/pIffyJJ+S0VcWKHntLVxqTdesmf26cbejHnxuJfUSIiOxwttbP/g8+9NNPwF13qQchN94oI4LkZI8GIY6SUC2PjR4tv+q1oV+wYyBCREHF1bV+o/R/CIoN1fLzgRdeANq1k1MKth56CMjMBB591ONvrSUw5Y68+mCyKhEFFVfX+o3Q/yEoEmr37pXNyfbuVY5VqCCnGQYO9Hh3VAutgWlCglymYbWV5zAQIaKg0ratvJk7W+v3df8He3kLloRav/8N3GwG3nwTmDxZtmu3dc89chnm5pt1PQ13AtPQUKBDB11OJyhxaYaIgkpoqPHX+l3NW3C0TGPoJZ1//gHat5fLMTZBiAgLw99D3sCKoelIP3Sz7udtCUztTbiwMZn+GIgQUdAx+lp/SRNqnZWi+owQwMKFQMOGsguqjfO1G6JTxW24df5z6N0v1Cvn7Q+BaaBjIEJEQSkhATh4EEhLA1JS5Fej9H8oSUKtYXukHD8OdO0KPPUUcPGi9VhICA50fwHV/92K7082tBryxnkbPTANdOwjQkRkMOnpchbDmbQ061wFw/ZIWbVKdkg9c0Y5dvPNMC/5GLF97/b5ebPlv+ewjwgRkR9zN2/BcD1Szp+XFTGPPaYehDz1FLBnDzYV2g9CAO+dtyUJtXdv+ZVBiHcwECEiMhh38xYM1SPlhx+ABg3khnU2rt5QDeYvvwbmzwcqVDDWeZPXMRAhIjIgd/IWDNEj5dIl4JlngI4dVadnPkcP1DyXidjh/9G8j4sReruQ5zFHhIjIwLTkLVhyRHy2H8q2bXIp5sABxdB5RGE45iIFfQCYuI9LgGOOCBFRgNCSt+CzUtSCAmDqVKB1a9Ug5HvchwbIQAr6ApAnwn1cyELXQGTGjBlo3rw5IiIiULVqVXTv3h0HVH5IiYjIM7xeirp/P9CmDTBtmqJrmjmsDEbiPXTCdziCGMVTuY8LATovzXTu3BmPP/44mjdvjmvXrmHSpEnIyMjAvn37UL58eafP59IMEZF7dC9FLSwE5swBxo8HrlxRjjdvjq97foyuz9Vz+lIpKXLGxyvnTV6h5f6t614z69ats/p+yZIlqFq1Knbs2IF27drp+dZEREFN1/1QDh8GBgyQlTFqb/zSS8DEiajwk2u3GO7jEty8uuldTk4OAKBSpUqq4/n5+cjPzy/6Pjc31yvnRURELhBCluOOHAn8799zK/XqAcuWAc2aAfCfDQbJt7yWrCqEwNixY3HPPfcgLi5O9ZgZM2YgKiqq6E9MjHJNkYiIfOD0admY7Ikn1IOQUaOAnTuLghCA+7iQa7xWvjt8+HCsXbsWP/30E6Kjo1WPUZsRiYmJYY4IEZEvrV0LDBoEnDihHIuOBpYuBe67z+7TU1NlnFK8rUhMjAxCmIQamAyTI2IxcuRIfPXVV9i4caPdIAQAwsPDER4e7o1TIiIiZy5cAMaNAxYsUB/v108mrFas6PBlEhJkrxDbJFRA7qvDxNTgpmsgIoTAyJEjsWbNGqSnp6NOnTp6vh0REXnKTz8BSUnAP/8ox268UbZnf/RRl1/ONglVbZYkOlou5XCWJLjomiMyfPhwfPLJJ0hJSUFERASOHz+O48eP4/Lly3q+LRERuSs/H3jhBaBdO/Ug5D//ATIzNQUhtlJTgR49lB3gs7Pl45bW7xQcdM0RMdnZOnLJkiXo37+/0+ezjwgRkRft3StbtO/dqxwrXx54+23gySftbwvsAksbenu77bKde2AwTI6IgbexISLyKL9uxGU2A7NnAy++KNu127rnHiA5Gbj55hK/1aZN9oMQwLrbKvuJBAev9hEhIgpEfp3v8M8/siT355+VY2FhwCuvyIRVD0VVx4559jjyf9z0joioBPw230EIYOFCoGFD9SCkYUO5m+7zz3t0aqd4F1VPHEf+z2t9RNzBHBEiMrKS5jv4bDnn+HGZ67F2rXLMZJLBx7RpgA7tFCz/z5x1W2WOiH/Tcv/mjAgRkZu05DvYSk2VN+T4eKBPH/k1NtYLMyirVgFxcepByM03Axs3AjNn6hKEAOy2SkoMRIiI3ORuvoNPlnPOn5cVMY89Bpw5oxwfPBjYs0cmpuosIUHGQ7VqWT8eHS0fN3xeDXkUk1WJiNzkTr6D2SwTW9WWJYSQswKjR8tOpB6bFfjhB6B/f/Xpm2rVgEWLZH8QFXotH9nrtsqZkODDQISIyE3u7C7r1fLVy5eB8eOB995TH+/RA/jwQ6ByZdVhvauBbLutUnDi0gwRkZvcyXfwWvnq9u1AkybqQUhUFLBsGfDZZw6DEL+sBiK/w0CEiKgEtOY76F6+WlAgK15atQL271eO33cfkJEhN6yz0yHV2fIRIJePzGY3z5GoGJbvEhF5gKu5FLqWr+7fL5uTbdumHCtTBnj9dWDECCDE8e+g6emyiseZtDQurZA6w7R4JyIKFq7mO1iWc3r0kEFH8WDE7fLVwkJg7lzZ/+PKFeV4s2ZyKaZePZdejt1PyZu4NENE5GUeLV89fBh44AHgmWeUQUhoKDBlCrB5s8tBCMDup+RdXJohooDkD5vQlegchQA+/VQuteTkKMdvv13OgjRv7tZ5sfsplQSXZogoqPnLJnTulq+aT5zGmV5Po+qGVeoHPPOM7I5atqzb5+Xx5SMiO7g0Q0QBJdDLTjdPWovTNRuoByHR0cD69TKKcDMIsWD3U/IWLs0QUcAo6SZ0hnbhArISxqHO+gWqw8vQDxU/noOuiRU9+rb+sMRFxsOlGSIKSl7tWqoT1Rv/lp8gkpJQ559/FMefxo0YinlINfVA9CTgwT6eDRTY/ZT0xkCEiAKGv5ed2ua2hCEfb0VMwbALb8CkMnn9Nf6DJ/ERTqA64AdBFpEaBiJEFDA8UXbqq6UIS26LJd5ogL1YhkTclbdXcewFlMcYvI2P8CQA6+6oRg2yiOxhsioRBQzLJnR2OpfDZAJiYqw3oSsuNVXmmMTHA336yK+xsfonuBZvqR4CM57DG9iG5rgLyiDkJ9yNhtiLjzAYtkEIwN4e5H8YiBCRz5jNsp348uXya0n3LnFnEzoLX1bbWHJb6uAfpKMD3sALCMdVq2PyEYZXys9EB2xAFm5WvIazIIvIqBiIEJFP6DX74E7Zqa83eTt2VOBJLMReNERb/KQY34sGaI5tODv4BRSaQjUHWURGxvJdIvI623wIC8sN1RN9KrTkevh0k7fjx3E6YTAqb/laMVQIE97A85iCabiKcKSlAWfPKpu1xcTIIIS9PcgoWL5LRIblbPbBZJKzD926ley3ey1lpz6rtklNBYYMQeXTpxVD/6AOkpCMn9BWLrtEXw+munVjbw8KHAxEiMirjNjrw+ubvJ0/L9uwL1umOrwAgzEOb+ICIlSXXdjbgwIJAxEi8ioj9vqwVNs42+TNI4mgP/wADBggoy0bVypWw9DQj5B85qGix6Kjjbfswm6r5EkMRIjIq4y4xbxXNnm7fBmYMOF6WY+thASUmTcPiypVQX8D3+T9ZUNB8h9MViUirzLyFvNqN1mPJIJu3w4kJgL79yvHIiOB998H+vWz3wDFILyRZEyBQcv9m4EIEXmd5YYGqM8++PKG5tFlh4ICYPp04JVX1Gt/770XWLIEuOmmEp2zNwT0hoLkcVru3+wjQkReZ+Qt5i2JoL17y69u31QPHADuvhuYOlUZhJQpI6dZ1q/3iyAEcD3JeM4c/fqtUGBiIEJEPpGQABw8KHtzpKTIr1lZATC1X1go78aNGgHbtinHmzYFdu6Ua0Ah/vNPsKvJw2PGeKctPgUOJqsSkc8EXBnq4cPAwIHA998rx0JDgcmTgUmTgNKlvX9uJaQledjSFt/Xs1vkH/wnHCci8gGX9sMRAvj0U6BBA/Ug5LbbgM2b5TKNHwYhgPMNBYvzRlt8ChwMRIiI7HBpP5zTp4GePWXVS06O8kWeeQbYtQto0cJLZ60PRxsKqinemI7IEQYiREQqXNqNd+1aOQuyapXi+SI6Gntmr8fyVu8ifWu5gJgZsJdk7Ig3G9ORf2IgQkRkw9l+OOXFBVx+Ygjw0EPA8eOKYw6164e4wgw0erajR3cWNgJLkvHbb7t2vDcb05F/YiBCRGTDUalqG/yM3bgLfS8uUA5WqoRfx32G2E3LsO9oRashq5kUPxcaCowc6ThnxGSSzeA80hafAhoDESIiG2rLCWHIx3RMwEa0wy34R3nAgw/CvCcTPVY+ZncmBQicBE5HOSMea4tPQYGBCBGRDdvlhDhkYCtaYAJmIhSF1oPlywPz5wNff41Nf9VweWfhQGDkxnTkP9hHhIjIhqVU9dgRM8bgLbyKyQjHVcVxonUbmD5OBm69FYAxdxbWW0IC0K0bd+Ml9zEQISKngm3b99BQYOHELJQbloR2UE5fXEVp/NH3ZcQlP2f1P8KIOwt7Q8A1piOvYiBCRA6p7Uhbqxbw1FNA3boBGJgIASxahM7PjwFwQTG8v3Qcsmcsw33jGinGLDMpznYWZgIn0XUMRIjILnvbvmdnA1OmXP8+OlomLvp9TsDx48DgwcDXXyuGhMmEwz2fRd3Fr6BeuXDVp1sSOHv0kEGH2s7CTOAkssZkVSJS5aiXhq2AKE1NTQXi4lSDENSpA9OGDbhpxRsItROEWDCBk0gbkxCu/DPjG7m5uYiKikJOTg4iIyN9fTpEQSU9XTbicpVl2SEry89+48/JkU0xli1TH3/ySeCtt4CICE0vG2x5NUTFabl/c2mGiFRprewoXprqN4mLP/4I9O8vT9xW1arARx8BXbu69dJM4CRyDQMRIlLlbmWHX5SmXr4MTJhwvSOXrYQEYN48oEoV1WHOdhB5DgMRIlLlrALEHsOXpm7fDiQmAvv3K8ciI4H335c76drpXa5WRRQwybpEPsBkVSJSpXXbd8PvLVJQgMKpL6OwVWv1ICQ+HsjIkEGKgyDE6Y68RKQJAxEissvVbd8NX5p64ADO1r8bIdOmIMR8zWrIHFZGnvj33wM33WT3JZztyAs430fGbJZJwMuXy6+BsOcMUUkxECHyQ968oVm2fU9LA1JSgGnT5FJEcYYtTS0sBN5/H9caNkalv7YphrejKRpc3YnUmFFAiON/Dh3tyAs430cmNRWIjZUTL336yK+xsZxFIWKOCJGf8UWOgm0FyKRJfpCseeQIMHAgsH694h+6awjFa5iEVzEZZlNpjB4t90txdA0l2UfGUWO4Hj0MGsQReQlnRIj8iFFyFCyBSe/e8quhghAhgE8/lc3J1q9XDB/AbWiDzZiKabiG0i7viOvuPjKeWNIhCmQMRIj8BG9oLjhzBujVS1a95OQohudgBBpjF7ahhWLM2YyHpYrIXuKuvWTdki7pEAU6BiJEfiJYbmhu5798+62cBfn8c8XQEdTC/fgOz2AOLqOc6tOdzXg4qiJylKxbkiUdomDAQITITwTDDc2thM4LF4AhQ4AHH5Sb1tko7NMXnWtm4AfT/apP11J27M4+Mu4u6RAFCwYiRH4i0G9obuW/bN4MNGoELFigHKtUCfjsM4R8+glennMDAG0zGfbYVhGlpcn9dewlm7q7pEMULLjpHZGfMJvl7IC9Tqd+u+kcrl+bvaUnxbVdvQpMmQK88YYs0bXVpQuwaJFVVKZWbRQTI4MQvStWLEEWYP3ZWYITVs1QoNFy/+aMCJGfcDdHwVtK0ttEU/5LRgbQogUwc6YyCClXTu4Rs3atYmpI60yGJ7mzpEMULNhHhMiPWG5oan1EvPGbvT0l7W3iSl5LCMyIWvAWsHqynBGx1aYNkJwM3Hqr3dfw5Y64CQmyV4nh+68QeRmXZoj8kJF2f7XXrEvLskN6ukxMtScWWUhGEtpBpSSodGng5ZeB557jXd0JI/3cUGDTcv9mIEJEbtOc2+HkdZT5LwIDsRjvYDQicEH5xLg4YNkymbBKDnHXYPIm5ogQkVd4qreJWv5LNRzHV3gYi/CkMggxmeQMyPbtDEJcYJSOvERqGIgQGYw/7dDqyd4mxRM6H0EqMhGHrvhaeWCdOsCGDbJiJjxc2wkHIXbkJaNjIEJkIP62Q6une5sk3JeDf+OTkIpHURlnlAcMGgTs2cOmGxoES0de8l8MRIgMwh+nzz3arOvHH4EGDRCy7GPlWNWqwFdfAR99BERElOicg00wdOQl/6ZrILJx40Z07doVNWvWhMlkwhdffKHn2xH5LX+dPvdIb5PLl4ExY4D77pO/mtt65BEgMxPo2tUTpxx0Ar0jL/k/XQORixcv4q677sL777+v59sQ+T1/nj4vUbOuHTuApk1ltGIrMhJYuhRYvRqoUsWDZxxc2GKejE7XhmZdunRBly5d9HwLooDg79Pnmpt1XbsGTJ8OvPKK/G9b8fHAkiVA7dq6nncwsMxa9eghgw61FvO+7MhLxM6qRAYQCNPnLnctPXAAeOIJYOtW5Vh4uGzd/swzQIi2CVs267LPqB15iQCDBSL5+fnIz88v+j43N9eHZ0PkPZbpc2cb2vn19HlhIQrf/wDi+ecRmn9ZOd6kiWxOVr++5pdmsy7n2GKejMpQVTMzZsxAVFRU0Z+YmBhfnxKRVxh9Q7sSO3IEJ5p0RsiokYogpDAkFHjxReCXX9wOQvyt2shXLLNWvXvLr37780QBxVCByIQJE5CTk1P057BaBj1RgArIHVqFAD79FFdvj0O1PesVwwdwG9oU/ozURi/LPWM08tdqIyK6zlBLM+Hh4Qhnp0QKYgE1fX7mDDBsGPDZZwhTGZ6DEXgBr+OKqRxGj5bXrfU6tVQb+WrXXSJyTNdA5MKFC/jrr7+Kvs/KysLu3btRqVIl3HTTTXq+NZHf8uVW9YCHkj6//RYYOBA4flwxdAS1MABL8D3ulw+UIFjw92ojItI5ENm+fTvii+3tPXbsWABAUlISli5dqudbE5GLigcef/4JLFxYgqTPCxeAZ58F5s9XHf4UfTAC7+M8blCMuRMsBEK1EVGw0zUQ6dChA4Ta4i0RGYJatYktS9Kn0zyVzZtlWe7ffyuGzqASnsaH+Bw97T5dS7BgCZ6ys2Wvs9OnA7jaiCjAGSpZlYi8x161iS2nSZ9XrwITJsi7vUoQIjp3QacamVhlUg9CtHb2LL4xYL9+wKlT9oMQwM+rjYiCAAMRoiDkqNpEjd0W85mZQIsWsglZYaH1WLlywLx5MH2zFpPel9MdJS1NdjV4Avy82ogoiDAQIQpCzqpN7CnK4zCbgdmz5T4xe/YoD2zTRj4+ZAhgMnmkNNmV4KlKFeCTT4C0NCAri0EIkT8wVPkuEXmHu1UkNWpA3uH79wc2blQeULo0MG0a8PzziimOkpYmuxI8nTolgx2W6hL5DwYiREFIaxWJyQRE1xJo9+dioOtoWR1jKy5Otmhv1AiA/TJgd4MEluoSBSYuzRAFIWdbwxdnMgFVxQn8Wr0bQp56UhmEmEyyZHfbtqIgpHhCaZ8+8mtsbMnarbNUlygwMRAhCkKO9raxNajSGvwbGYca2/+rHIyNBdLTgVmzgDJlAOi394uz4Elr9Q0RGQMDEaIg5SiBdNo04POPcnDsgf5YeCYB4bmnlS8waJBMSG3XrughPfd+CfiNAYmClEkYuONYbm4uoqKikJOTg8jISF+fDlFAUs3l2PCjTEhV23iyalXZfvXhhxVD6elyGcaZtDT3c0XUmrDFxMgghFUyRMag5f7NZFWiIOI0gfTyZeDZifKurqZ7d2DBAlknq8IbCaUBtTEgETEQIQoWajMJVvvI7NgBJCYCv/+ufHJkJDBnjhx3kFTirYRSX28MSESewxwRoiDgKIG016PXsK/3K0CrVupBSHw8sHev3EfGSWYrE0qJSCsGIkQBzlECaV1xAD/hbtRf8RJw7Zr1YHg48NZbwPffA7Vru/ReTCglIq0YiBAFOLWOpCYUYjjexy40RktsVT6paVNg505gzBggRNs/E55o505EwYM5IkQBzjYxtBaOYDEGohPWKw8ODQUmTgRefFG2a3cTE0qJyFUMRIgC3PXEUIHeWI65GI4bcF5x3KXouii3ahnQsqVH3pcJpUTkCi7NEAW4tm2BuBpnsBK9kIK+qkHIkgojEL5vt8eCECIiVzEQIQpwod99i22X49ATnyvGjqAWOuE7RCXPQWhEOR+cHREFOwYiRIHqwgVg6FDgwQdR5vxxxfCn6IMutTIwdPX9TCAlIp9hjghRINq8Wfb9+PtvxVBBxA34dcA81HqkJ3YzgTQg2OuYS+QPGIgQBZKrV4GpU4HXXwcKC5XjXbqg9Ecf4Z6aNb1+aqQPpx1ziQyOgQhRoMjMBPr1kzvi2ipXTjYne+opp91RjYi/8auzdMy1bVaXnS0fZ98W8gfMESHyd2YzMHu2bEKmFoS0bi0fHzLEL4OQ1FQgNlZ2mu/TR36NjZWPBzNHHXMtj40eLY8jMjIGIkT+7OBB4N57geeek8syxZUuDUyfLqcSbr3VJ6dXUo72yOnRI7iDEbWOucUJARw+LI8jMjIGIkT+SAhg8WKgYUNg40bleFwcsHUrMGGC365h8Dd+x2w75pb0OCJfYY4IkQ1v5yNofr8TJ4DBg4H//lc5ZjIB48YBr7wClCmj2zl7g5bf+IOxg+v1jrmeOY7IVxiIEBWjdwWCbdBx+rTcV87l91uzRiacnj6tHIuNBZKTgXbtSn6iBsDf+B1r21b+rGRnq88amUxyvG1b758bkRZcmiH6H73zEdSSLh97zLX3M5/NwfHO/WV0ohaEDBokE1IDJAgB+Bu/M6GhMmAFlDnIlu/fecdvV+YoiJiEUIuljSE3NxdRUVHIyclBZGSkr0+HApjZLIMEe0sBlt8us7Lc+4fdXpmlPcXf7+dX03DzK/0RbT6kPLBqVWDhQuDhhwOuxNXymTj7jd/dzyRQqM3ixcTIIISlu+QrWu7fDESIAKSnyxkKZ9LStOcjOAty7CmDy0hrNRGtfnlHdTwVjyBs8Xw8NKBKwDa1sgRwgHUwYvmNn30ypEALQsn/abl/M0eECPrmIzhLulTTBDuwDImo/8vvirEcRGIk5uATJCJ6iglXIoCePdWbWj36KDBtGlC3rn/eoBISZLChFmTxN/7rQkODM2GXAgMDESLom4+gJXgJxTVMwAy8hJdRGtcU4z8iHgOwBIdQG4CsGhk2zHGJ65Qp1x/zx1mShASgWzf+xk8UqBiIEEHfCgRXg5e6+APLkIiW2KoYu4JwjMdMvIdnIGxyzE+dcv1c/LX1N3/jJwpcrJohl5jNMo9i+XL5NdCaSOlZgWAJcux3VxcYhrnYjUaqQcgONEET7MS7GK0IQrRiIzAiMhoGIuRUsOz1YclHqFXL+vHo6JLNIDgKcmoiG+vQGXMxAuVw2WpMhIbi3YjJaIMt+B31Fa9rMgFVqmg/H7b+JiIjYSBCDgXbXh8JCXL7lrQ0ICVFfs3KKvkyhlqQ8ziWY58pDg/gO+UT6taF6eefEbP0FRSYwuzO0syd62y2xb5gbQRGRMbC8l2yS+/eGsHIbAa2rD2LmBnDUPuXleoHDR8OvP46UL48AOd9IuyVuDrjTikyEZEr2EeEPELP3hpBa906YOBA9emImjWBJUuATp0UQ876RKgFK/YwgCQivbGPCHmEkfb68PuGTRcvAs8+C8ybpz7eu7dcZ7nhBtVhZ1UjtiWuf/4JTJ0qx9QagbH1NxEZBQMRsssoe334c9dQsxnYM/8X1H05EREn/lIecMMNMjjp2bPE72UbrMTFsREYERkfl2bILiPs9WFvjxZ/aPG9ZuVVHBk8DcPyZiIUhcoDOncGFi2SSzI68fuZJCLyS8wRIY/x5V4f/pws+/07mbhxTCIaY7di7CLK4Y+n3kTjeUPcK3chIjI4Lfdvlu+SQ3r11nCFsz1aDNkPw2xG4aw30XZMU9UgZAtaoTF2o9u3Q2EuZBBCRMQcEXLKV3t9uJoEu3q1/OrzZYeDB4GkJIRs3Ihwm6EClMIUTMMbeB5mlAL+F0Cx2oiIgh0DEXKJL/b6cDUJ9v335R9vJ7AW5V8cFWi8Zylu/3AUTHl5iuN+Q330wyfYjcZWj7OhGBERl2bIwJzv0WLNm91eLW3ve8afRNm+j6DeGwMVQUghTJiNcWiKHYogBNC/2oiIyB8wECHDcrRHixpvbehmSeBteuQLZCIO3fGl4piDqI14pOE5zEY+yliNmUyyM6o7O/kSEQUaBiJkaPaSZe3RO4HVbAYmj8zBYtEfX+ARVMUpxTGLMBANsRcb0V4xxoZiRETWGIiQ4RXfiG7ECNeeo1f+RcZ7afj2aEP0R7Ji7CSqoBu+wJNYhDyol6t5o9qIiMifMFmV/ELxZNn333d+vMfzL65cASZORKO331YdXoPuGIL5OIWqquMjRgCPPmqAyh4iIoNhIEJ+xZLA6qzbq0fzL3buBBITgX37FEO5iMAzeA/JSAJgP5Hl0UdZqktEpIZLM+RXHCWwejz/4to14NVXgZYtVYOQNHRAA2QgGf1hLwhhYioRkWMMRMjveKXb6x9/APfcA7z4ogxIijGXDsdYvIWO+AGHUNvuSzAxlYjIOQYi5JeKJ7CmpMivWVkeCEKEAD74AGjUCPj1V+V448YI3bUD96weg5rR1n99bIMNJqYSETnHTe+ILLKzgYEDge++U46FhAATJ8oZkrAwAMqdbdu0ATZv5k63RERa7t9MViUCgOXLgWHDgPPnlWN16wIffwy0amX1sFrbeyakEhFpw0CEgortLEbbO8/CNGIYQj5bqf6EYcOAN94Aypf37okSEQUJBiIUNFJTgVGjgCNH5PcPYB3qmQaiulB2P7tcqSbKLl8CdOrk5bMkIgouTFYlVWYzkJ4uVyzS0/Xdu8UbLPvDHDkClMNFfICnsQ5dVIOQFPRG9NkMpF5gEEJEpDcGIqRg2Vk2Ph7o00d+jY31zq62ejCb5UyIEEArbMFuNMLTmKc47ixuQC+sQF+k4Jypku6b5xEREQMRslF85qC47Gz5uD8GI5s2ASeOXMWrmISfcA/q4i/FMd+iM+KQic/QC4D+m+cREZHEHBEqUnzmwJYQskHX6NFAt27GLEtVJKL+r3z20tZM/IpENMZuxXMuohzG4U3MxxCodUfVa/M8IiKSGIjYYe+mFsg2bVLOhBRXfJbAaGWqtomoAHBTLTP+2/EddE6ZiBBcVTxnC1rhCXyMv1DX7ut6fPM8IiKywkBEhdpNLTpa7nESyF0yXf3t32izBJblpOIzObVxEMnZSWiYvFFxfAFKYQqm4Q08D7OdvwK6bJ5HREQKDERsqN3UgOs5EoHcstvV3/7tHeeLWSTlcpJAfyzFuxiFSOQpjs/EnUjEMuxGY7uvyT1iiIi8h8mqxTjLkQAQ0JUUbdvKWQDbXW0tHO0k66tKm+LLSVVwEmvwCJZgoCIIESYT/nh4HLrX2m4VhNx4o/xTHPeIISLyHs6IFOPPORKeEBoql5969JBBR/GAzNEsgS9nkSzLRA/jSyzEYFTFKcUxB1Ebf01ORseX2+OAyqwN4HwmJxhzhoiIvMErMyIffPAB6tSpgzJlyqBp06bYZNCaSH/NkfCkhAQZONSqZf24vVkCX88iRUfmYjEG4Et0Vw1CFmEgGmIvSt3bHsD1/WF695ZfQ0PVHysu0PqqEBEZie6ByMqVKzF69GhMmjQJu3btQtu2bdGlSxccOnRI77fWrKQ5EoEiIQE4eBBISwNSUuTXrCz1WQ0ts0gel56Oe4Y3xAAsVQydRBV0wxcYbFqEijGRbiedBmJfFSIiIzEJofa7rOe0bNkSTZo0wYcfflj02B133IHu3btjxowZDp+rZRthTzCb5W+62dnqv+FbKimysjgtb7F8uZwlcCYlRc44eMSVK8DEicDbb6sOr0F3DMF8nDZVBeD+0pDl58FeoMWfByIidVru37rOiFy9ehU7duxAJ5uNwzp16oTNmzcrjs/Pz0dubq7VH2+y5EgAyoRNVlKo8/os0s6dQNOmqkFInikCSViKBKTiFKqWOOnUp7M9RERBQtdA5PTp0zCbzahWrZrV49WqVcPx48cVx8+YMQNRUVFFf2JiYvQ8PVVacySCXUkqbTS5dg149VWgZUtg3z7leIcOKPdXBgakJSElxVS0nNStm/ub9zFniIhIf16pmjHZ3KWEEIrHAGDChAkYO3Zs0fe5ubk+C0a6dWOVhCvcrbTR5I8/gCeeAH79VTkWHg7MmAGMGoXQkBB0uPn6UEkb0zFniIhIf7oGIpUrV0ZoaKhi9uPkyZOKWRIACA8PR3h4uJ6n5DJLJQU5Z5lFUrvpv/NOCWaRhAA+/BB47jng0iXleOPGwLJlwJ13KoY8UVJsme1xljPE7qtERO7TdWkmLCwMTZs2xfr1660eX79+Pdq0aaPnW5OXaam0cUl2NtClCzB8uDIICQkBJk8GfvlFNQjxVEkxc4aIiPSn+9LM2LFjkZiYiGbNmqF169ZYsGABDh06hKFDh+r91uRlHptFWrECGDYMOHdOOVa3LvDxx0CrVnaf7snGdLrN9hAREQAvBCK9evXCmTNn8PLLL+PYsWOIi4vDN998g9q1a+v91uRvzp6VMyArVqiPDx8OvP46UL68w5fxdJIpc4aIiPSjex+RkvB2HxHyoXXrgIED1aODmjWBJUsAmzJwe9LTZfdTZ9LSmAdERKQHw/QRIXLq4kW5DNOli3oQ0rs3kJnpchACeLGkmIiISoyBiIeZze73rQg6v/wiK1+Kdd0tcsMNcokmJUX+twZMMiUi8h8MRDyIm6O56OpVWfVy993An38qxx94QM6C9Orl9luwMR0RkX9gjoiH2OtbYfkNnDe///ntNyAxEdi1SzlWrhwwezYwdKj9dRWNzGYmmRIReZuW+zcDEQ/g5mguKCyU6yETJwL5+crxVq1kWW7dul4/NSIi8iwmq3oZN0dz4t9/gXvvBcaNUwYhpUoBr70m/+cwCCEiCjpe2Wsm0Bl5czSfLk0IASQnA888A+TlKcfvvFO2aG/c2EsnRERERsNAxAOMujlaSTd9K5GTJ4EhQ4AvvlCOmUzA2LFyN90yZVSfztwOIqLgwKUZDzBi3wpL8qztkpFl0zddK3m+/BKIi1MPQmrXlp3EZs+2G4Sw+oiIKHgwEPEAo/Wt8NSmb5rl5sruqN27A6dOKccHDAD27gXat7f7Ej4NoIiIyOsYiHiIkfpW+CR5dsMGoGFD2YrdVpUqcnZk8WLAQfa0zwIoIiLyGeaIeJBRNkfzavLslSuyOdlbb6lHEN26AQsWAFWrOn0pT+6aS0RE/oGBiIeFhvr+Jum15Nldu4B+/YB9+5RjERHAe+8BSUkuNyczcvURERHpg0szAUj35Nlr12TvjxYt1IOQ9u1lLkj//po6pBq1+oiIiPTDQCQA6Zo8++efMoKZPFkGJMWFhwNvvgn8+KMsc9HIiNVHRESkLwYiAcrjybNCyF1yGzWSu+baatQI2LFD9gcJce/HymjVR0REpD/uNRPgPNIYLDsbGDQI+L//U46FhADjxwNTpgBhYR45Z7VGbDExMgjhxoFERMbHTe/Ic1auBJ5+Gjh3Tjl2661yo7rWrT3+tuysSkTkv7Tcv1k1Q+rOngWGDwdWrFAff/ppYNYsoHx5Xd7eCNVHRESkPwYipPTdd7IL6tGjyrEaNWTTsgce8P55ERFRwGGyKl138aKcBXngAfUgpFcvIDOTQQgREXkMZ0RI+uUX4IknZHmurRtuAD74AHj8ce+fFxERBTTOiAS7q1eBF18E7r5bPQjp1AnIyGAQQkREuuCMSDDbtw9ITAR27lSOlSsHzJ4NDB2qqTsqERGRFgxEglFhoWzKMXEikJ+vHG/ZEli2DKhb1yunw1JdIqLgxUAk2Pz7r9yIbsMG5VipUsDUqcALL8j/9gK15mXR0bLDKpuXEREFPuaIBAshgKVLgQYN1IOQ+vWBrVuBSZO8GoT06GEdhACykWuPHnKciIgCGwORYHDypJxeGDAAyMuzHjOZ5P4wO3YAjRvrfipmM5CeDnz6qUw/Uevra3ls9Gh5PBERBS4uzQS6r74CBg+WwYit2rXlLImXWpiqLcPYIwRw+LDMHWGHVSKiwMVAJFDl5gJjxgCLF6uP9+8vEzG8tIePZRlG685Gx47pcz5ERGQMDEQC0caNMiH14EHlWJUqwIIFQPfuXjsds1nOhLizvWKNGp4/HyIiMg7miASSK1eA556TaxlqQcjDD8sW7V4MQgC5vOLKckxxJhMQEyNLeYmIKHBxRiRQ7Nolm5P99ptyLCJCLsP07++T5mRal1csp/jOO+wnQkQU6Dgj4u+uXQOmT5dNyNSCkPbtgb17ZcWMjzqkal1eiY4GVq1iHxEiomDAGRF/9tdfcqO6LVuUY+HhMkAZPRoI8V28aTbLP5UqAWfP2j+uShXg7beBWrXYWZWIKJgwEDEAzS3OhQDmzwfGjQMuXVKON2okW7THxel1yi5xpVzXMkkzbx5nQIiIghGXZnwsNRWIjQXi44E+feTX2FgHXUWPHgUefBB4+mllEBISIjuj/vqrIYIQta6ptrgMQ0QU3Dgj4kP2emtYWpwrbtCffSYDELU1jltvBT7+GGjdWtdzdoUr5bqVKsnL6dCByzBERMGMMyI+4uhmrWhxfu6cnC7p1Us1CPmz49PY+N5umFv4PggBXCvXPXtWBiAMQoiIghsDER9xdrO2tDjPfOs7ucyyfLnimBMhNfAA1uG27z9A+wfLO17S8SJXy3XZNZWIiBiI+Iizm3A5XMT7GI67nn9A5oXYWIFeuKMwE9/hgaLHjhwBHn1UdnZPT/fdhnGuluuyayoRETEQ8RFHN+GW+AW70BjD8YFiTFSsiOGVlqM3VuAcKqk+/513XEh6LcayI+7y5Z4JYNq2lUmo9tqWsGsqERFZMBDxEbWbdWlcxct4ET/jbtyGP5VP6tQJWxZk4oOzj7v0HpakV0fBiOaqHReEhspGroAyGGHXVCIiKo6BiI/Y3qzvwD5sQWu8iFcRikLrg8uWBebOBdatw7/Xarn8HoqkVxv2SmxdCWCcSUiQVT+1bE6X5bpERFScSQh39kT1jtzcXERFRSEnJweRXtqu3ttSVxUi48l38ULOBJRBvvKAli1lc7K6dQHIpZP4eO3vk5YmS2UtzGY582EvYdZkkkFDVlbJZi40N2sjIiK/p+X+zT4ivvTvv0iY2x8JOenKsVKlgKlTgRdekP/9P5Ylnexsx306bNkmx7patbNpk3UAo1VoaMmeT0REgY1LM74gBJCcDDRsKKc4bNWvL7ujTppkFYQAjvMvHLFNjmWJLRERGQEDEW87dUrW2PbvD+TmWo+ZTMDYscD27UCTJnZfwl7+hRp7FSossSUiIiNgIOJNX30lm5OtWaMcu+km4McfgTffBMqWdVpSm5AAHDwocz9Gj5aPaalQYYktEREZAQMRb8jNBQYNArp1A06eVI737w9kZBQlU7haUmvJv3j7bWD1am0VKiyxJSIiI2DVTAm4VBGycSOQlCSnL2xVqQIsWAB07170kL2N8CzBgaPSV3cqVFJT5Z43xRNXY2JkEMISWyIicoeW+zcDETep3cCjo+UsQ0ICgCtXgMmTgbfeUi9vefhhYOFCoGrVooe8VVJriyW2RETkSSzfdaKkN157sxaWRmDrZ+3GfUsTgcxM5ZMrVJDRyoABijURb5XU2mKJLRER+UrQBSJOZzKcMJvl89UmOULENbyAN9D22akACpQHtGsny3ZjY1VfmyW1REQUbIIqWdUTLc3tzVrcgr+wEe3wGiYhzDYICQsDZs+WJS52ghCAJbVERBR8giYQcTST4WxPluKUsxECQzAPe3AX2mCL8gmNGgE7dgDjxgEhjv93s6SWiIiCTdAEIlryLxwpPhtRHcewFv/BPDyN8rhk/XohIbIz6q+/yt4hLmBJLRERBZugCUQ8lX9hmbXoic+QiTg8iG8Vx2SVuhWFG34CXn1VLsto4E+71jprukZERORM0CSreir/IjT3HH6uMwI3HUlRHf8QT6Nm8izUuae8xjO8LiFB9j4zckltSZN+iYiIgCDqI2Lp0WFv11qXenSsXy/LbrOzFUNHUQMvVF6MR+Z3DvgbcUmarhERUeDTcv8OmqWZEuVfXLoEjBgBdOqkGoT826on/vkiA0uPB34Q4qmkXyIiIiCIAhHAzfyLX38FGjcG5s5VjlWsCKSkoPaWlbin242GWjrRi6eSfomIiIAgyhGxcDn/oqAAeOUVYPp09V/vO3UCFi9WRjUu8OeW6my6RkREnhR0gQjgQkvzffuAxERg507lWNmywKxZwLBh9ht+OODvSZ5sukZERJ4UVEszThUWykSRpk1Vg5DcO1ri61d3I/3O4TAXuheElLSzq6+x6RoREXkSAxGLQ4eAjh2BMWPkzrnFFIaWwqzIV1Dp95/QddxtiI+XFThaAodASfJk0zUiIvIkBiJCyI3oGjSQe8HYyI2+Ay3Mv+D53MkwF1vJ0jqLEUhJnv7UdI2IiIxN10DktddeQ5s2bVCuXDlUrFhRz7dyz6lTwKOPAv37A7m51mMmEwpHj0VTsQM70FTxVK2zGIGW5JmQABw8KGO3lBT5NSuLQQgREWmja7Lq1atX8dhjj6F169ZYtGiRnm+l3VdfAYMHAydPKsduuglITsZGdMBf79h/ieKzGA6TXxGYSZ5Ok36JiIic0DUQmTZtGgBg6dKler6NNrm5Mg9k8WL18f79ZZJDVBSOLXftJV2ZxbAkeTrr7MokTyIiCiaGKt/Nz89Hfn5+0fe5tsslJVVQALRsCezfrxyrXBlYsAB45JGihzw5i2FJ8uzRQwYdxYMRJnkSEVGwMlSy6owZMxAVFVX0JyYmxrNvULo0MGiQ8vGuXYHMTKsgBPB8qSqTPImIiKxpDkSmTp0Kk8nk8M/27dvdOpkJEyYgJyen6M/hw4fdeh2Hxoy5HjlUqAAsWgR8+SVQrZriUD1KVd1N8jSbgfR0YPly+dXoZb5ERESu0Lw0M2LECDz++OMOj4mNjXXrZMLDwxEeHu7Wc10WGirLdUeOBObMAerUcdhy3TKLodYN9Z133JvF0Jrk6e/dWImIiOzRHIhUrlwZlStX1uNcvKdOHeDrrwG4dpN3eX8aHVi6sdomuFr6mLizpOPPe90QEVFg0TVZ9dChQzh79iwOHToEs9mM3bt3AwBuvfVWVKhQQc+3domWm7y7paoluek768ZqMsk+Jt26uf6anF0hIiIjMQmhdpvzjP79+yM5OVnxeFpaGjq4cFfPzc1FVFQUcnJyEBkZ6dFzM5tlm3Z73U4t5bRZWe7PFpT0pp+eDsTHOz8uLc21IMle4GXJd2HCLBEReYKW+7euVTNLly6FEELxx5UgRG96t1z3xAZ3nuzGGih73RARUWAxVPmuN+nZct1TN31P9jEJpL1uiIgocARtIKJny3VP3fQ92cck0Pa6ISKiwBC0gYinm5UV56mbvif7mATiXjdEROT/gjYQ0aNZmYUnb/qe6saqZ+BFRETkrqANRAD9Wq7r0RrenW6sxekZeBEREblL1/LdktKzfLc4PRp8WapmAPUN7nxVKqtWUhwT436XWCIiIlta7t8MRHRk1Js+O6sSEZGeGIgYCG/6REQUbLTcv3Vt8U7ut4YnIiIKBkGdrEpERES+xUCEiIiIfIaBCBEREfkMAxEiIiLyGQYiRERE5DMMRIiIiMhnGIgQERGRzzAQISIiIp9hIEJEREQ+Y+jOqpbu87m5uT4+EyIiInKV5b7tyi4yhg5E8vLyAAAxMTE+PhMiIiLSKi8vD1FRUQ6PMfSmd4WFhTh69CgiIiJgMpk8+tq5ubmIiYnB4cOH/XZDPUd4ff4v0K8x0K8PCPxr5PX5P72uUQiBvLw81KxZEyEhjrNADD0jEhISgujoaF3fIzIyMmB/wABeXyAI9GsM9OsDAv8aeX3+T49rdDYTYsFkVSIiIvIZBiJERETkM0EbiISHh2PKlCkIDw/39anogtfn/wL9GgP9+oDAv0Zen/8zwjUaOlmViIiIAlvQzogQERGR7zEQISIiIp9hIEJEREQ+w0CEiIiIfCZgA5HXXnsNbdq0Qbly5VCxYkWXniOEwNSpU1GzZk2ULVsWHTp0wG+//WZ1TH5+PkaOHInKlSujfPnyePjhh3HkyBEdrsCxc+fOITExEVFRUYiKikJiYiLOnz/v8Dkmk0n1z6xZs4qO6dChg2L88ccf1/lq1Llzjf3791ecf6tWrayO8dfPsKCgAC+88AIaNGiA8uXLo2bNmnjiiSdw9OhRq+N8+Rl+8MEHqFOnDsqUKYOmTZti06ZNDo/fsGEDmjZtijJlyuDmm2/GvHnzFMesXr0a9evXR3h4OOrXr481a9bodfpOabm+1NRU3H///ahSpQoiIyPRunVr/N///Z/VMUuXLlX9O3nlyhW9L0WVlutLT09XPff9+/dbHWekzw/Qdo1q/56YTCbceeedRccY6TPcuHEjunbtipo1a8JkMuGLL75w+hxD/B0UAeqll14Sb731lhg7dqyIiopy6TkzZ84UERERYvXq1SIjI0P06tVL1KhRQ+Tm5hYdM3ToUFGrVi2xfv16sXPnThEfHy/uuusuce3aNZ2uRF3nzp1FXFyc2Lx5s9i8ebOIi4sTDz30kMPnHDt2zOrP4sWLhclkEn///XfRMe3btxeDBw+2Ou78+fN6X44qd64xKSlJdO7c2er8z5w5Y3WMv36G58+fFx07dhQrV64U+/fvF1u2bBEtW7YUTZs2tTrOV5/hihUrROnSpcXChQvFvn37xKhRo0T58uXFv//+q3r8P//8I8qVKydGjRol9u3bJxYuXChKly4tVq1aVXTM5s2bRWhoqJg+fbr4/fffxfTp00WpUqXEL7/8ovv12NJ6faNGjRKvv/662Lp1q/jjjz/EhAkTROnSpcXOnTuLjlmyZImIjIxU/N30Ba3Xl5aWJgCIAwcOWJ178b9HRvr8hNB+jefPn7e6tsOHD4tKlSqJKVOmFB1jpM/wm2++EZMmTRKrV68WAMSaNWscHm+Uv4MBG4hYLFmyxKVApLCwUFSvXl3MnDmz6LErV66IqKgoMW/ePCGE/KEsXbq0WLFiRdEx2dnZIiQkRKxbt87j527Pvn37BACrH4QtW7YIAGL//v0uv063bt3Evffea/VY+/btxahRozx1qm5z9xqTkpJEt27d7I4H2me4detWAcDqH1JffYYtWrQQQ4cOtXqsXr16Yvz48arHP//886JevXpWjw0ZMkS0atWq6PuePXuKzp07Wx3zwAMPiMcff9xDZ+06rdenpn79+mLatGlF37v675M3aL0+SyBy7tw5u69ppM9PiJJ/hmvWrBEmk0kcPHiw6DEjfYbFuRKIGOXvYMAuzWiVlZWF48ePo1OnTkWPhYeHo3379ti8eTMAYMeOHSgoKLA6pmbNmoiLiys6xhu2bNmCqKgotGzZsuixVq1aISoqyuXzOHHiBNauXYtBgwYpxj799FNUrlwZd955J5599tmiXZC9qSTXmJ6ejqpVq+K2227D4MGDcfLkyaKxQPoMASAnJwcmk0mx/Ojtz/Dq1avYsWOH1f9XAOjUqZPd69myZYvi+AceeADbt29HQUGBw2O8+VkB7l2frcLCQuTl5aFSpUpWj1+4cAG1a9dGdHQ0HnroIezatctj5+2qklxf48aNUaNGDdx3331IS0uzGjPK5wd45jNctGgROnbsiNq1a1s9boTP0B1G+Tto6E3vvOn48eMAgGrVqlk9Xq1aNfz7779Fx4SFheGGG25QHGN5vjccP34cVatWVTxetWpVl88jOTkZERERSEhIsHq8b9++qFOnDqpXr47MzExMmDABe/bswfr16z1y7q5y9xq7dOmCxx57DLVr10ZWVhZefPFF3HvvvdixYwfCw8MD6jO8cuUKxo8fjz59+lhtVuWLz/D06dMwm82qf3/sXc/x48dVj7927RpOnz6NGjVq2D3Gm58V4N712XrzzTdx8eJF9OzZs+ixevXqYenSpWjQoAFyc3Px7rvv4u6778aePXtQt25dj16DI+5cX40aNbBgwQI0bdoU+fn5WLZsGe677z6kp6ejXbt2AOx/xt7+/ICSf4bHjh3Dt99+i5SUFKvHjfIZusMofwf9KhCZOnUqpk2b5vCYbdu2oVmzZm6/h8lksvpeCKF4zJYrx7jC1esDlOep9TwWL16Mvn37okyZMlaPDx48uOi/4+LiULduXTRr1gw7d+5EkyZNXHptR/S+xl69ehX9d1xcHJo1a4batWtj7dq1iqBLy+u6ylufYUFBAR5//HEUFhbigw8+sBrT+zN0ROvfH7XjbR935++kXtw9l+XLl2Pq1Kn48ssvrQLQVq1aWSVT33333WjSpAnmzJmD9957z3Mn7iIt13f77bfj9ttvL/q+devWOHz4MGbPnl0UiGh9TW9w93yWLl2KihUronv37laPG+0z1MoIfwf9KhAZMWKE0+z/2NhYt167evXqAGSEWKNGjaLHT548WRQNVq9eHVevXsW5c+esfqM+efIk2rRp49b7Fufq9e3duxcnTpxQjJ06dUoRuarZtGkTDhw4gJUrVzo9tkmTJihdujT+/PNPj9zEvHWNFjVq1EDt2rXx559/AgiMz7CgoAA9e/ZEVlYWfvzxR6dbd3v6M1RTuXJlhIaGKn5LKv73x1b16tVVjy9VqhRuvPFGh8do+RnwBHeuz2LlypUYNGgQPv/8c3Ts2NHhsSEhIWjevHnRz6u3lOT6imvVqhU++eSTou+N8vkBJbtGIQQWL16MxMREhIWFOTzWV5+hOwzzd9Bj2SYGpTVZ9fXXXy96LD8/XzVZdeXKlUXHHD161GeJjr/++mvRY7/88ovLiY5JSUmKSgt7MjIyBACxYcMGt8/XHSW9RovTp0+L8PBwkZycLITw/8/w6tWronv37uLOO+8UJ0+edOm9vPUZtmjRQjz99NNWj91xxx0Ok1XvuOMOq8eGDh2qSJTr0qWL1TGdO3f2WbKqlusTQoiUlBRRpkwZp0mDFoWFhaJZs2ZiwIABJTlVt7hzfbYeffRRER8fX/S9kT4/Idy/RktibkZGhtP38OVnWBxcTFY1wt/BgA1E/v33X7Fr1y4xbdo0UaFCBbFr1y6xa9cukZeXV3TM7bffLlJTU4u+nzlzpoiKihKpqakiIyND9O7dW7V8Nzo6Wnz//fdi586d4t577/VZ6WfDhg3Fli1bxJYtW0SDBg0UpZ+21yeEEDk5OaJcuXLiww8/VLzmX3/9JaZNmya2bdsmsrKyxNq1a0W9evVE48aNvX59Qmi/xry8PDFu3DixefNmkZWVJdLS0kTr1q1FrVq1AuIzLCgoEA8//LCIjo4Wu3fvtioVzM/PF0L49jO0lEYuWrRI7Nu3T4wePVqUL1++qMJg/PjxIjExseh4S+ngmDFjxL59+8SiRYsUpYM///yzCA0NFTNnzhS///67mDlzps/Ld129vpSUFFGqVCkxd+5cu6XUU6dOFevWrRN///232LVrlxgwYIAoVaqUVYBq1Ot7++23xZo1a8Qff/whMjMzxfjx4wUAsXr16qJjjPT5CaH9Gi369esnWrZsqfqaRvoM8/Lyiu51AMRbb70ldu3aVVRVZ9S/gwEbiCQlJQkAij9paWlFxwAQS5YsKfq+sLBQTJkyRVSvXl2Eh4eLdu3aKSLgy5cvixEjRohKlSqJsmXLioceekgcOnTIS1d13ZkzZ0Tfvn1FRESEiIiIEH379lWU0dlenxBCzJ8/X5QtW1a1r8ShQ4dEu3btRKVKlURYWJi45ZZbxDPPPKPow+EtWq/x0qVLolOnTqJKlSqidOnS4qabbhJJSUmKz8dfP8OsrCzVn+niP9e+/gznzp0rateuLcLCwkSTJk2sZmGSkpJE+/btrY5PT08XjRs3FmFhYSI2NlY1QP7888/F7bffLkqXLi3q1atndaPzNi3X1759e9XPKikpqeiY0aNHi5tuukmEhYWJKlWqiE6dOonNmzd78Yqsabm+119/Xdxyyy2iTJky4oYbbhD33HOPWLt2reI1jfT5CaH9Z/T8+fOibNmyYsGCBaqvZ6TP0DJzY+9nzqh/B01C/C8zhYiIiMjL2EeEiIiIfIaBCBEREfkMAxEiIiLyGQYiRERE5DMMRIiIiMhnGIgQERGRzzAQISIiIp9hIEJEREQ+w0CEiIiIfIaBCBEREfkMAxEiIiLyGQYiRERE5DP/D6CktJBa2iRCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_data,y_data,color='blue')\n",
    "plt.plot(x_data,2*x_data+1.0,color='red',linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "x= tf.placeholder(dtype=tf.float32,name=\"x\")\n",
    "y= tf.placeholder(dtype=tf.float32,name=\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "w= tf.Variable(1.0,dtype=tf.float32,name=\"w\")\n",
    "b= tf.Variable(0.0,dtype=tf.float32,name=\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(x,w,b):\n",
    "    return tf.multiply(x,w) +b\n",
    "\n",
    "pred = model(x,w,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sess = tf.Session()\n",
    "\n",
    "\n",
    "init = tf.global_variables_initializer() \n",
    "sess.run(init)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "saver = tf.train.Saver\n",
    "ckpt_dir = \"D:\\desktop\\ml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_epochs = 10\n",
    "learning_rate =0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "loss_function = tf.reduce_mean(tf.square(y - pred))\n",
    "\n",
    "\n",
    "# loss_function = tf.reduce_mean(tf.pow(y - pred, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model_of_Epoch_1 saved!\n",
      "Model_of_Epoch_2 saved!\n",
      "Model_of_Epoch_3 saved!\n",
      "Model_of_Epoch_4 saved!\n",
      "Model_of_Epoch_5 saved!\n",
      "Model_of_Epoch_6 saved!\n",
      "Model_of_Epoch_7 saved!\n",
      "Model_of_Epoch_8 saved!\n",
      "Model_of_Epoch_9 saved!\n",
      "Model_of_Epoch_10 saved!\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "ckpt_dir = r\"D:\\desktop\\ml\" \n",
    "\n",
    "with tf.Session() as sess:\n",
    "\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    for epoch in range(train_epochs):\n",
    "\n",
    "        for xs, ys in zip(x_data, y_data):\n",
    "            _, loss = sess.run([optimizer, loss_function], feed_dict={x: xs, y: ys})\n",
    "\n",
    "\n",
    "        b_temp = b.eval(session=sess)\n",
    "        w_temp = w.eval(session=sess)\n",
    "\n",
    "\n",
    "        plt.plot(x_data, w_temp * x_data + b_temp)\n",
    "\n",
    "   \n",
    "        saver = tf.train.Saver()  \n",
    "        saver.save(sess, save_path=ckpt_dir + f'\\\\Model_of_Epoch_{epoch+1}.ckpt')\n",
    "        print(f'Model_of_Epoch_{epoch+1} saved!')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: 2.0\n",
      "b: 1.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "\n",
    "sess = tf.compat.v1.Session()\n",
    "\n",
    "w = tf.compat.v1.Variable(2.0)\n",
    "b = tf.compat.v1.Variable(1.0)\n",
    "\n",
    "sess.run(tf.compat.v1.global_variables_initializer())\n",
    "\n",
    "print(\"w:\", sess.run(w)) \n",
    "print(\"b:\", sess.run(b)) \n",
    "\n",
    "sess.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xXfGYyWtbO2TkaOUnECljvvvuO30AAQC9e/fGF198YVAnMDAQcrkc/v7+BsMhCoUCMpnM4Fhqaio+++wzXL9+HSEhIQCAadOmIT4+Hps3b8aiRYuwevVq9OrVCzNnzgQANG7cGAkJCaVeTl1YWIi1a9eiQYMGAIBx48bhnXfe0ZcvWLAAy5cvx8CBAwEAYWFhSElJwbp16xiIEFG5Ziz/hyV0k1fjhrRB1KPrQHQ0cOGCqN6jSt5Y3HU4PmnTB4LM/ECHLUNGjsRAxEW6deuGuLg4/fd+pUytm5SUBEEQ0LhxY4Pj+fn5qP731s4XLlzA888/b1AeGRlZ6kDE19dXH4QAQHBwMLKysgAAt27dQnp6OkaNGqXv1QGAoqIiTnglogqhZP6PK7dzserARQCme0kEAF7qQmRMeBPC0e2QqdWiOmdCmmJan8lIC6xlth0yaBOgSaaCdyEGIi7i5+eHhg0b2u16Go0GcrkcZ86cEU141fW8CBJdefbg6elp8L1MJtPfS/P3vJn169ejffv2BvXcfWIuEZG9lMz/0URZxWwvSdOsNKzYswLhWWniQi8v4D//wa2eLyNv7x+Amd4Wk6ngXaz8BCLVq2vnaLi6DXbm5eUFdYkoWOpY69atoVarkZWVhX/961+S1woPD8fx48cNjpX83t4ee+wx1KpVC5cvX8bgwYMdei8iorJC10uycv9FrDl0yaBMrlHj9RM7MennT+GlKRKf3LYtsHUr0Lw5ogD0aFHLINvq3YcFWLDHwlTwbqD8BCIeHnadKOou6tWrh6NHj2LQoEHw9vZGUFAQ6tWrhwcPHuDHH3/E448/Dl9fXzRu3BiDBw/GsGHDsHz5crRu3Rq3b9/GwYMH0aJFCzz77LOYMGECOnbsiP/+978YMGAA9u3b55R0+/Pnz8eECRMQEBCA3r17Iz8/H6dPn8bdu3cxZcoUh9+fiMgdyT1k6NQwyCAQaZCdjuV7VqJVxkXxCZUqAXPmALNmAcV6okv2tgBArwhxKnh36wnRKT/Ld8upd955B1euXEGDBg1Q4+9Aq2PHjhgzZgxeeukl1KhRA//9738BaJfIDhs2DFOnTkWTJk3Qr18/nDhxAqGhoQCADh06YMOGDXj//ffRqlUr7Nu3D7Nnz3b4M7z66qvYsGEDtmzZghYtWqBLly7YsmULwsLCHH5vIiJ3plvi6yFoMOrU19izZaJkECJERAAnTgBz5xoEIcbogpP+rWohskF1tw1CAEAmOGrigB3k5ORAoVBApVIhICDAoCwvLw9paWkICwuDj497zQCm0uG7JaKK5Mj3iaj82mi0u/6bqEwt88DVkW+g/gfvAt7eLmidbUx9fpdUfoZmiIiIyhJBANauRZc33wQePhQVXw2qjZur4hA5+DkXNM55GIgQERHZmVojmJ6jkZ4OjBoF7BfvEQMAGcNfR+33l6NuldKldigLGIgQERHZUXxyhmhpbrBu1UpzpXbFy8SJQE6O+OR69YDNmxHctavT2utqDESIiIjsJD45AzHbkkSJyjJVeZiz9ke0+e1j1Dws3QuC118Hli0D/P0d3k53wkCEiIjIDtQaAbG7UySzpfa5cBQL9sWhWt59cWGtWsDGjUCvXg5vozsq84GIGy/6IRvxnRJRWXQy7Y4oU2q1XBUW7F+L537/SfqkYcOA1auBqlUd30A3VWYDEV1a8dzcXFSuXNnFrSF7ys3NBSBOHU9E5M5K7mr7zJ8nsPiH91Hj4T1x5Zo1gY8+Avr3d07j3FiZDUTkcjmqVq2q31zN19cXMpn7Jmwh8wRBQG5uLrKyslC1alXuRUNEZYpuV9uAvAeY++N6/F/yj5L1bj/bH0FbNwBBQc5sntsqs4EIACiVSgDQByNUPlStWlX/bomIyop2YYHon5WMmV8sQ/CDbFH5XR9/rOg/AfM/XQBYmOnU7DLgcqBMByIymQzBwcGoWbMmCgsLXd0csgNPT0/2hBBR2XP/PuRvvonVm9dJFv/Y4EnMjJqABWOetjiQMLkM2A03r7NVmQ5EdORyOT+8iIioVGzufThyBBgxAkhLExXlePninWdew7FOfbCgX3OLAwhTy4BjtiUhbkibchOMlItAhIiIqDRs6n149Ah46y1g1SrJ4nuduuDk2//Fvxs3wFIrhlRMLQMWAMgAxO5OQY9wZbkYpuHuu0REVKHpeh9KLr3V9T7EJ2eITzp+HGjVSjoI8fMDPvwQVX86hJ6921m9+63UMuDiBAAZqjycTLtj8TXdGQMRIiKqsMz1PgDa3ge15u/v8vO1vSCdOgEXL4pP6twZ+OUXICYGsHElZ8llwKWt5+44NENERBWWNb0PkTnXgOho4Px5cUVvb2DRIu0eMqWcs6hbBmyveu6OPSJERFRhWdKrUEldhKrLlwDt2kkHIU8+CZw9C0yZUuogBNAuAw5W+MBYf4oM2vkr7cICS30vd8BAhIiIKixzvQoNb1/Dzm1volncu0BRkWGhpyewcCGQkAA0a2a3Nsk9ZJjXNxwARMGI7vt5fcPLxURVgIEIERFVYMZ6Hzw0arx2Yif2bJmIxzP/FJ/YsiVw6hTw1ltQe8iRmJqNb87dQGJq9j/zSUohKiIYcUPaQKkwDJSUCp9ytXQXAGSCG+8wlpOTA4VCAZVKhYCAAFc3h4iIyiHdqhlAOyek7t2bWL5nJZ64cUFcWS4HZs0C5swBvLwcnnSsrGZWtebzm4EIERFVePHJGXjnm2Q8c3gnZh7ZAt/CfHGlpk2BrVu1c0VgPOmYLkwobz0X1rDm85tDM0REVOFF+Rfg2MEleOfAOnEQIpNpJ6ImJemDEKuX/ZJRDESIiMhtqTWC3edfGBAEYONGoEULyA4eFBfXr4/kT7/FN0OnIPFmrv7+FS3pmCMxjwgREbklh2/6dvMmMHo0sHevZPHVl6Ixoun/4fI5GXDunMH984s0Ft2ivCQdcyT2iBARkUuY6u2wKe26pQQB+PRTCBER0kFIaChOfbQdXeu9gMt5hhNDdfe/cvuhRbcqL0nHHIk9IkRE5HSmejt6hCsdt+nbrVva9Os7d0omDLs+4CUEb1qLCWvPQIC4N0N3/89OXoMywAd/5eRJtlMG7VLb8pJ0zJHYI0JERE5lrrdjzcE/HTP/YtcuoHlzYOdOUVGWXzWM+vdc/KvJUKw5k2X2/pk5+Xi5XR0AFSPpmCMxECEiIqexZLXJ5mNXLLqWxfMv7t4FhgwBBg7U9oiU8G2zp9Bz1Af4sWE7q+5fL8i3wiQdcyQOzRARkdNYstrk3qNCi65l0fyLvXuBV18FMsRzSu5UDsDsnm9gb9PONt8/skF19AhXlsmkY+6CgQgRETmNpb0YVSt7QvWo0Pb5Fzk5wNSpwIYNksX7GnXAW73G4rZftVLfX+4hQ2SD6iafh4xz6NDM4sWL8eSTT8Lf3x81a9bEgAED8McffzjylkRE5MYsXUUyolMYABvnXxw8CLRoIRmEFPkHYEqfyXjt+beNBiGlvj9ZxaGByJEjRzB27FgcP34c+/fvR1FREXr27ImHDy1b9kREROWLpVvcj+ve0Pr5Fw8fAuPHA08/DVy7Ji7v1Quy5GQkduoDmUy6BZbc/4NXWkNR2ctxSdYqGKfuNXPr1i3UrFkTR44cwVNPPWW2PveaISIqf0puMqcjtUeLxZu+JSQA0dHApUuiogdelfF+nzFoHTsNUS1CSnX/uw8LsGCPA5OslRNuu+ndpUuX0KhRI5w/fx4RERGi8vz8fOTn/5PjPycnB6GhoQxEiIjKGbtlTc3Lg2bOHMhWrIBMI852ejw0AtOenYQbVZUA/gkybLk/N7mznFsGIoIgoH///rh79y5++uknyTrz589HbGys6DgDESKi8qfUW9yfOYP7L70C/9SLoqJHlbyxtEs0trZ9DoJMOwtBN8n05xndIfeQWXV/tUZA56UHja74KXntis4tA5GxY8diz549+Pnnn1G7dm3JOuwRISIiswoKgIULoVm4EB5qtag4KaQJpvaZgrTAWpKnfza6g9WrXBJTs/Hy+uNm69ly7fLImkDEKct3x48fj2+//RZHjx41GoQAgLe3N7y9vZ3RJCIiKovOn9fOBTl7VrTaIl9eCas6D8a6dgOh8ZAbvYQtG9FZeg43ubOeQwMRQRAwfvx47Nq1C4cPH0ZYWJgjb0dEROVVURHw7rvAvHnaHpESkh9rgCl9JuNijXpmL2XLRnSWnsNN7qzn0EBk7Nix+PTTT/HNN9/A398fmZmZAACFQoHKlSs78tZERFRe/PGHthfkxAlRUZHMA2s6voQ1kS+hSG76I600G9Hplh1nqrjJnb05NI9IXFwcVCoVunbtiuDgYP3Xjh07HHlbIiKykVojIDE12z1yZGg0wKpVQKtWkkHIxep1MGDYCqzqPNiiIASwPRGZ3EOGeX3DDa5lr2tXdA4fmiEiorLBbktq7SEtDRgxAjhyRFwmk0EzbRpGV+mGa7niJbtSHgvwxsvt6iC/SIPE1Gyb9oOJighG3JA2op+RknlESsWpeUSsxYRmRETO4TY5MgQBWL8emDJFmym1pIYNga1bgY4dTSYmEwBMfqYR6gX54crtXHx28hoyc+wTYJV62XEFYM3nt0OHZoiIyP2pNQJid6dIzn3QHYvdneL4YZrr14HevYHXX5cOQsaPB375BejYEcA/PRRSadjXDmmDic80hnclD6w6cNEgCAGATFUeYrYlIT5ZvCuvObpN7vq3qoXIBtUZhJQSd98lIqrgTqbdMZqoC9AGIxmqPJxMu+OYHBmCAHzyCTBhAqBSicvr1gU2bwa6dQMg7pE48mY3nLl6V9RDYS7AkkEbYPUIVzKYcCEGIkREFZxLc2T89Ze2B+Sbb6TLR4/WLtv9u3vf1DyW/q0ME5i5PMAii3BohoiognNZjowvvwSaN5cOQkJCgL17gY8+MghCYrYliYILY8MsTEJWNjAQISKq4HQ5MowNTsig7XWwW46M7Gzg5ZeBF17Q/ndJQ4YAycna+SJ/s2UeC5OQlQ0MRIiIKjin5sjYvRuIiAC2bxeX1agBfPWVdr5ItWoGRdYMs+g4PcAimzAQISIikytQSi7dtSnpmUqlzQvSrx/wd5ZtAwMHAr/9Bjz/vOTptgyzMAlZ2cDJqkREBEAbjPQIV5rMkWFT0rP9+4FRo4D0dHFZtWrAmjXaoRqZ8YDA1mEWJiFzf0xoRkREFrEm6ZlaI+B08jXU/M9chH3xsfQFn31Wm7wsJMTsvdUaAZ2XHjS718vPM7pL9nAwCZlzWfP5zR4RIiIyy5LJojN3noe/jydUuYX4ds0OzPpiKerekxiG8ffX7iEzYoTJXpDidMMsMduS9JlTdSwZZtElISP3wx4RIiIySteTcOzSLaw5lGq2vndhPqb99AlGnfoGHhJhS3b7zqi+Y5s2SZmJ+9l1aIicjj0iRERUalIf+qY8fvMPLN+zEg3vXBeV5Xp6Y0nXETjQZSB+Cq0DuYX3KxlkWDKPhcoWBiJERCRibD6IFE91ISYc246Y41+gkiDeDfdUrXBM6zMJV6uFAPcLJDOZGrufLllZ8fknHGYpXxiIEBGRAVPzQUpqlnUZK75bgWa3rojK8uWeePdfQ7Hxyf7QePzTB1JyKS73hKnYGIgQEZEBc8nDAECuUWPM8S8x8dhn8NIUicp/UTbC1D6TcSmojqis5BJb7glTsTEQISIiA+aShzW4nY7le1egVcaforJCDzne6zgIcR1eQJHc8CNGt8S2ZCZT7glTsTEQISIiA8aSh3lo1Bh5+hu8efQTeKsLReUXatTDtD6T8dtjDURlppbYck+Yio2BCBERGdDt0VI8eViduxl4d+9KtLueIqqvlnkgrsP/4b2OL6OgkqfkNU1lMpW6X3HGelKofGAgQkRUQViaXbR48jAPQYNXzn6Ptw5vgm9hvqjug3oNMLH3JPwYEKY/FqzwwZw+zVDNz9uiJbalTVZGZRsTmhERVQC2JAI7vP8UfN8Yg3aXksSFMhkwaRKwcCHU3j52yevBZGXlhzWf3wxEiIjKOWv2iAEACAKwdSswcSKQkyO+YFgYsGUL8NRTdm8r94QpH5hZlYiIANiQoyMjA3j9dWD3bukLjhkDLFsGVKnikPYyWVnFw0CEiKgcszhHx+VsRJ4+AIwdC9y5I65YuzawcSPQs6fjGksVEgMRIqJyzJLcG4G5KtSJGQ4c2CNdITpau1tu1ar2bBoRAAYiRETlmrncGz0vJmLhDx+gRu49ceFjjwEffQT06+eYxhGBgQgRUblmLEdHQN4DzDuwDv/+7ZD0iS++CHzwARAU5JR2UsXl4eoGEBGR4+hydAD/rJLpcvkM9m18QzoICQwEtm8HduxgEEJOwR4RIqIyxtolrlERwYgb0gbLvjyNUV9/iFd+iZeu2K8fsG4doFQ6qOVEYgxEiIjKEFuTfkXd/gO9tk6A7MoVcaFCAaxeDQwbpk1URuREHJohIiojdInJSi7HzVTlIWZbEuKTM8Qn5eZqM6B26yYdhPToAZw/r10ZwyCEXICBCBFRGWAuMRmgTUym1hSrkZgItGql7e0oyc8PWLsW+OEHIDTUAS0msgwDESKiMsDixGRpd4D8fGDWLKBzZ+DPP8WV//Uv4NdftRlU2QtCLsY5IkREZYAlickAIP/kKWDAm0BysrjQxwdYvBiYMAHw4N+h5B4YiBARlQHmEpNVUhfhjeNf4KnlO4CiInGFdu20G9k1bao/xA3myB0wECEiKgOMJSYDgEa3rmL53pVomXlJfKKnJzB/PjB9OlDpn//l27r6hsje2DdHRBWGWiMgMTUb35y7gcTUbMOJnW7CWBulEpN5aNR4/cSX+G7rROkgpFUr4PRp4K23REGI1atviByEPSJEVCGUhR4Ac23UJSaL3Z0Cn7RUvLtnJdre/F10HUEuh+ytt4DZswEvL4Myc6tvZNCuvukRruQwDTkFe0SIqNwrCz0AlrYxKvwxHPP7DQc+mSQZhPxZPRSjXl+N+BdjREEIYOXqGyInYCBCROWaTfk3nMziNl5OA555Bh4TJ0Ke98igngYyrGs3EM8NX41D/vWMBliWrr6xtB5RaTk0EDl69Cj69u2LkJAQyGQyfP311468HRGRSFnoATDbRkHAU0e/AVq2AA6JN6q7UjUYLwxeisXdRiK/kpfJAMvc6htr6xGVlkMDkYcPH+Lxxx/HmjVrHHkbIiKjykIPgKl717yfjc1fzsfS+Pchf/hQVL61TR/0HvE+ztQONzhuLMDSrb4xNvtDBu28lHZhgVY+hWllYaIwuYZDJ6v27t0bvXv3duQtiIhMKgs9AJL3FgQMSDmM2P1rocgXByC5ylp4tUsMEuq1MnntkkGObvVNzLYkyACD4SBdcDKvb7hdJ6qWhYnC5DpuNUckPz8fOTk5Bl9ERKXhqh4AKcZ6BUq2sfrDe1j79SKs+m65ZBCCUaOQ/P3PZoMQQDrI0a2+USoMy5QKH8QNaWPX4KAsTBQm13Kr5buLFy9GbGysq5tBROWIK3oApJjrFdC1sfcfx/CfHz5A9UcSf4gplcCGDUCfPmirERD8fapkgjNA+2xKEwFWVEQweoQrHZpZlUuFyRJu1SMya9YsqFQq/Vd6erqrm0RE5YAzewCkWNIrEBXijWO/bkDc14ulg5BXXgF++w3o0weAdIIzHUsDLLmHDJENqqN/q1qIbFDd7sFAWZgoTK7nVj0i3t7e8Pb2dnUziKgccnQPgLF9WyzpFdi/bBN67f8AIRniYQohKAiytWuBf/9b8pl0Cc6Kf+Ar3WT+RVmYKEyu51aBCBGRI+l6AOzN1LCLorKX0V6BKvm5mH1wAwb9uk/6ws8/D82HcTj5sBKyzt2QDJ6MBVgAkJia7dIN7crCRGFyPYcGIg8ePMClS//sf5CWloZz584hMDAQderUceStiYicQjfsUrLHI0OVhzHbktC1cZDkeR2vnMN/v1+N2jm3xIVVqwLvv4/4x7sjdnOy2dUmJQMsd1mlYmqjPsD8PBaqGGSCIDhsMffhw4fRrVs30fHo6Ghs2bLF7Pk5OTlQKBRQqVQICAhwQAuJiGyn1gjovPSgyXkQJVUuyMPMI5sRnbRHukLv3sD69Yi/6yEZ4Oj6NIzNbTEWGJk7z1F07QGkJwo7uz3kHNZ8fjt0smrXrl0hCILoy5IghIjI3ZmbjFlS2+sp+H7zeMkgRKhSBVi/HtizB+rgEJvS0rtjOntXTxQm98c5IkRENrJ0kqV3UQGm/LQNo0/ugodEmJD9ZEdU//x/QL16AICTl7MtXm1SfEjGmlUqjpgrY4wzlgpT2cVAhIjIRpZMsmyZcRHL96xEo2xxOoI8T29cnjob4QvfAjz+6aC2dbWJO69ScdREYSr7GIgQEdnI1GRMT3UhxiXswNjEz1FJ0IjOvd/qCfh+tg3hTZuIymxdbcJVKlQWuVVCMyKissRYUrGmWWn4+uOpmJiwXRSE5Msr4eqbc+F/+jjQuLFFKd9LMpaW3p3S2RNZioEIEVEpFJ+MKdeo8Ubi5/h262Q0z7osqnv+sQYYOTYOtZfMR/yFLHReehAvrz+OidvP4eX1x9F56UHEJ2fYnDXVHtlWiZzNoct3S4vLd4morFCnXEDuK0Pg/0uSqKzQQ473Ow5CXIcX8H50OwCwaImtrflA3CWPCFVc1nx+MxAhIioNjQZYvRp46y0gTzwJ9I+gOpjSZwruNInAvL7h6BGuNJl7RJfk6+cZ3fUp4m1ZbWLreUT2YM3nNyerEhHZ6vJlYMQI4OhRUZHg4YGbo8fj4sgJmF1doQ8EElOtW5pr62oTrlKhsoKBCBGRtQQBWLcOmDYNePhQXN64MWRbt6JWhw6oVaLInZfYErkCAxEiIhNEQxyVHkL+2mhgn5GN6iZMABYvBnx9JYu5xJbIEAMRIiIjDCZ9CgL+nXwQEQc/gn+eRC9IvXrA5s1A164mr8mN4IgMcfkuEZEE3WZtGao81HhwF+u/WoDle1dKByGvvQb8+qvZIATgEluikhiIEBGVUHzzuOcuHMUPm8aix6WTonpCSAiwd692voi/v8XX50ZwRP/g0AwRUQkn0+4gL+MvrNm/Fs/9/pNknZ3NuyH04/Vo16aBTffgRnBEWgxEiKhcKk0eDY/d32Lfpimo8fCeqOyWb1W83Wss9jWOxGqP0k0o5RJbIgYiRFQO2ZxZ9N49YNIktN+6VbJ4T5NOmNPzDdzxVQDgyhYie2AgQkQu44jsn7pJpiVXpGSq8hCzLclgDkbx+zc6l4hmcyZDdv266Jr3fKpgTo8Y7G72FCDTti/QzxOZOXlITM3mkApRKTAQISKXcMR+KMUnmZYkQLsqJXZ3CnqEK7E/JROxu1OgunUXbx3ahPBz30te88cGT2Jm1HjcqmK4nPbOw0JM3nHOLu0mqsi4aoaInK740tjidL0W8ckZNl33ZNodi9Knrzl4CTHbkhCafBrfbx6PIVJBiL8/zr+zHLNHLhIFISWVtt1EFRl7RIjIqazptbB2uMPStOifHv4db/+4ESNPfwsPiZacbNgGbfd/hRb16uLnv4dvMlWPsGDPBdx5WGD3dhNVZOwRISKnsrTX4mTaHauuq9YIuH0/32y9Vjf/wKdxb+DV09+IgpBcT2/M7hGDlwbOx0l1FQD/rGxRKipLBiGlbTdRRcceESJyKkds+iY136Qkr6JCTDz2Kcac2Am5oBGVn6oVjml9JuFqtRDJ+3OzOiLHYCBCRE5lj03fiq92uXI7F6sOXJQc6tEJ/+syVuxZgaa3rojK8uWeWPbUUGx6oj80HnKj9+dmdUSOwUCEiJyqtJu+WdL7oVNJXYSY419gQsJ2eGrUovJzwY0w9dkpSA0KNXt/blZH5BicI0JETlWaTd+MrbaR0vD2Nezc9iam/vw/URCiqeSJd/81FP835F1REGLs/tysjsgxGIgQkdPZsumbqdU2xXlo1Hj15FfYs2UiHs/8U1yhZUt4nD6FiA+XokY1P4vvb2u7icg0mSAI5n6vXSYnJwcKhQIqlQoBAQGubg4R2Zk1mVUTU7Px8vrjJq9X9+5NvLtnFZ68kSIulMuBmTOBuXMBLy+r729ru4kqIms+vzlHhIhcRmrTN2Mf8qZWo8gEDQaf/R5vHd4E30LxEl6haVPItm4F2rUze39b201EtmEgQkRuw1Tad2OrUUJysvDfvavR+eovojINZLg67DWErV0JVK7ssHYTke04R4SI3IK5tO93H+YjWOHzz0RRQcALv+7DDxvHSgYh1wODcWrLVwjbupZBCJEbYyBCRC5nLu07ACzYcwFz+mhXrdR8cAcbd76DZd+/B/+CR6JzMl8ZgeC0P9A+eoDD2kxE9sGhGSJyOUvTvlfz9cTXgVcR9t5MBDy6L65YuzawaROUPXo4rrFEZFcMRIjI5SxJix6Yq0KdmOGodWCPdIURI4CVKwGFolRt4YoYIudiIEJELmcuLXrPi4lY9MMaBOWqxIVKJbB+PfDcc6Vuh6nJsswRQuQYnCNCRC6nS59est8hIO8BVny3HB/tWigdhAwaBCQn2y0IMTVZNj45o9T3ICIxBiJE5HJS6dO7XD6DfRvfwMDfDolPqF4d+Pxz4LPPtP9dSpZMlo3dnQK1xm3zPxKVWQxEiMgt6NKn1/fRYFH8Gmz9Yh6UD+6IK/bvD/z2G/DCC3a7t6WTZU+mSbSHiEqFc0SIyG1E3f4DvbZOgOzKFXGhQgG89x4wdCggs+/kUUsmy1pTj4gsxx4RInK93Fxg4kSgWzfpIKRnT+1ckGHD7B6EAOYny1pbj4gs55RA5MMPP0RYWBh8fHzQtm1b/PTTT864LRHZiVojIDE1G9+cu4HE1Gz7zpVITARatdL2dpTk5wesXQvEx2tzhDiIscmyOjJoV8+0Cwt0WBuIKiqHD83s2LEDkyZNwocffohOnTph3bp16N27N1JSUlCnTh1H356ISslhS1rz84F584BlywCNRlzepQuwaRNQv77t97CQbrJszLYkyACDSau64GRe33DmEyFyAJkgCA6dBt6+fXu0adMGcXFx+mPNmjXDgAEDsHjxYpPnWrONMFFF54hEXLolrSX/J6G7atyQNrYFI2fOANHR2kmnJfn4AEuWAOPHAx7OHT1mHhEi+7Dm89uhPSIFBQU4c+YMZs6caXC8Z8+eSEhIcOStiSoUYx+gc/o0QzU/b5uCE3NLWmXQLmntEa60POApLAQWLtR+FRWJyzt0ALZsAZo0sex6dhYVEYwe4UpmViVyIocGIrdv34ZarcZjjz1mcPyxxx5DZmamqH5+fj7y8/P13+fk5DiyeUTlgrFeiwxVHt749KzBMWWAN15uVwf1gvzMfshas6Q1soEFuTySk7W9IElJ4jIvLyA2Fpg2Dajk2sV8cg+ZZc9DRHbhlN94WYlZ7oIgiI4BwOLFixEbG+uMJhGVC6Z6LaRk5uRj5YE/9d+bGnaw25JWtRp4911g7lygoEBc3ro1sHUr0KKFRfcjovLFoQOwQUFBkMvlot6PrKwsUS8JAMyaNQsqlUr/lZ6e7sjmEZV55notzDGVvtwuS1ovXgT+9S9g5kxxECKXa4OT48cZhBBVYA4NRLy8vNC2bVvs37/f4Pj+/fvRsWNHUX1vb28EBAQYfBGRcaVNsGUqfXmplrRqNNrluK1aaZfnlhQerg1AYmO1wzJEVGE5fEr6lClTsGHDBmzatAkXLlzA5MmTce3aNYwZM8bRtyYq9+yRYMtY+nKp/V90pJa06nKN7Nt7HKqOT2kTlD16VOJEGTB9unbVzBNPWNxGh+YxISKXcvgckZdeegnZ2dl45513kJGRgYiICOzduxd169Z19K2Jyj1dr0WmKs/ieSLGSPWu6PZ/KbkiR1libkl8cgZiv/0NXY9+g7cPbUSVgkeia6FhQ+2KmE6drGoXl9QSlW8OzyNSGswjQhWBLfk/ip9z5XYuVh24CAClCkY+G93B6GoRU22MT87A/Lh9WPL9++iadkb64uPGaXOD+PlZ1SaH5TEhIodymzwiRGSaLX/tS51T1dcTAHAvt9DqNsig7eEwlb7c2JJWtVqD4++8hx++fR+K/Iei8hsBNbD4helYvXqq1bk4HJLHhIjcDgMRIhcx9te+biWL1F/7xs5R5RZCADD5mUb6HCF3HxZgwZ4Uk6tqbE1frtYIOHvqdyimjMf8hB8l63zWsicWdn8VD7x9MdjSXCPF2D2PCRG5JQYiRC5gy1/7lpyz/VQ6fp7RXX9OrwilwRDOZyevITPH+FwPS8QnZ+DoorWY+vUqVH8kTjr4V5VAzIgaj8MNntQfs2V1j93ymBCRW2MgQuQCtvy1b8s5JYdUxnVvWKr05T8eS0H+6Dew6MIRyfJd4V0x/5nXoarsb3DcltU9dsljQkRuj4EIkQvY8te+PXoISpO+XL37O7QcPBw17meLym77KvB2z7H4oYlhfiBL5p8YY25FUGmuTUTuw7lbWxIRANv+2ndZD4FKBYwaBXm/vpJByPeNO6LXyA8kgxDA+vknOtbmMSGisomBCJEL2JK1tFSZTm3144/a9OubNomKVN5+mNB3GmIGzEK2X1VRuVLhU+rltbo8JkqFYXBlj2sTkXvg0AyRC+j+2o/ZlgQZDPN/GPtr35ZzbPbwITBjBvDBB5LFh+q3xYyoCcjyFw/zjOvWEJ0aBqFt3Wo4c/Uuvjl3w6b5KDpREcHoEa4s1dwWInJfTGhG5EQlE4NJLbG1JY+IXTON/vwzMHw4kJoqKnroVRnvdB+NHS17aNO1F6Obs/HzjO7Yn5LJbKhEFZg1n98MRIicxFgAMadPM1Tz87Y5s6rdegjy8oDZs4EVKwCp/y10747DM5ZgxMEsANI9MnFD2gAAs6ESVXAMRIjcjNunKj91CoiOBi5cEJdVrgz897/AG28AHh4me2R6hCvReelBk8uMA/08Mee55lAGcIiFqLxiIELkRtQaweSHc/EhDad/KBcUAAsWAIsXA2q1uLxjR+1GdY0aGRw21iOTmJqNl9cft/j2HK4hKp+s+fzmqhkiB7MmEZlT/for0K4d8J//iIMQb29g2TLg6FFREAL8k4+kf6taiGxQXR9AWZvlVJfOPj45w+bHIKKyjYEIkYPZIxGZWiMgMTUb35y7gcTUbKg1pejILCoCFi0CnngC+OUXcXnbtkBSEjBtGiCXW3Vpa3OY6J4idndK6Z6JiMosLt8lcrDSJiKz6yqZ33/XzgU5eVJcVqkSMHcuMHMm4Olp3XX/Zi4bqhRuXkdUsbFHhMjBSpOITDfJteTQjtVDGhoNsHIl0Lq1dBASEaE9PmeOzUEIYDobqjncvI6oYmIgQuRg1qYq1w3D7Eq6jrd2JRvdbRewcEgjNRXo2hWYMkW7RLc4Dw9g1izg9GltkGIHxrKhmsPN64gqJg7NEDmB7sO55BCLssQQi9QwjDFmhzQEAVi7FnjzTW2m1JIaNwa2bgU6dLD1sYwqng01U/UIC/ZcwN2HBdy8johEGIgQOYm5VOXGco2YIzmkkZ4OjBoF7N8vfdKkScDChYCvr2OSo8Fwp9/KXnLnpKYnojKHgQiRExX/cC5OrREQuzvF6iAEKDGkIQjaXo6JE4GcHHHlsDBg82agSxcATkgX/zdLe4SIqOJhIELkBszlGpEiGtLIzAReew3YvVv6hNdf1+YG8fcHYLwHRjcR1t7ZXrl5HRFJYSBC5AasXTEiGtLYsUObgv2ORFK0WrWAjRuBXr30h0z1wAh/Xz92dwp6hCvtGigY6xEiooqLq2aIHMjSRGTWrhhRKny0PRZKT+DFF4FBg6SDkGHDgORkgyAEcONsr0RU4bBHhMhBrJl/YUkiMNFmcd/tBp4eDWRliSvXrAl89BHQv7/kteyR7ZWIyB7YI0LkANYmIjOXa0QGYNHzLfB861qIrC6HfMRwbZAhFYS88ALw229GgxCg9NleiYjshYEIkZ2Zm38BSCciM5YITD8MExEM7NunzYL68cfiiwcGAtu3A59/DgQFmWxjabK9EhHZE4dmiOzMmvkXJSduGl1Z8vABMGYMsG6d9EWfew5Yvx5QKi1qo64Hhrk9iMjVGIgQ2Vlp51+IVpYcOQKMGAGkpYkrBwQAq1YBw4cDMuuCBub2ICJ3wECEyM7sNv/i0SPgrbe0gYaUHj20y3JDQ61rYDHM7UFErsZAhMjOzK2AsWhvlRMngOho4I8/xGV+fsC772oTlFnZCyKFuT2IyJU4WZXIzqzdbddAfr62F6RjR+kg5F//An75RTtfxA5BiBRLc58QEdkDe0SIHMCm+Rdnz2p7Qc6fF5f5+ACLFmn3kPFw3N8Pztp7hohIRyYIgtv+uZOTkwOFQgGVSoWAgABXN4fIahbtbFtYCCxZArzzDlBUJL5Iu3bajeyaNnVoW43tPaNrrb33niGi8suaz2/2iBA5kNn5Fykp2l6Q06fFZZ6ewLx5wIwZQCXH/qq6au8ZIiLOESFyBbVaO+G0TRvpIOTxx4FTp4C333Z4EAJw7xkich32iBA526VL2rwfx46Jy+RyYNYsYM4cwMvLaU3i3jNE5CoMRIicRaMB4uKA6dOB3FxxedOm2rkg7do5vWnce4aIXIVDM0TOcPUq0LMnMG6cOAiRyYBp07SrZlwQhADce4aIXIeBCJEjCYI2+2mLFsCPP4rL69cHjh4Fli3TLtF1kVLlPiEiKgUGIkSOcvOmdjO6V18F7t8Xl48dC/z6K9C5s/PbJsGi3X+JiOzMoXNEFi5ciD179uDcuXPw8vLCvXv3HHk7IvcgCMBnn2mHYe7eFZeHhgKbNwNPP+38tpnBvWeIyNkcGogUFBTghRdeQGRkJDZu3OjIWxG5h1u3gJgYYOdO6fKRI4EVKwCFwrntsgL3niEiZ3JoIBIbGwsA2LJliyNvQ+Qedu3SbkR365a4TKkENmwA+vRxfruIiNyYWy3fzc/PR35+vv77nJwcF7aGyEJ37wLjxwP/+590+csvA++/D1RnLwMRUUluNVl18eLFUCgU+q/Q0FBXN4nItL17gebNpYOQoCDgiy+ATz9lEEJEZITVgcj8+fMhk8lMfp2WSlltgVmzZkGlUum/0tPTbboOkcPl5ACjR2uHWjIyxOX9+wPJycD//Z/z20ZEVIZYPTQzbtw4DBo0yGSdevXq2dQYb29veHt723QukdMcPAiMGAFcuyYuUyi0wzBDhmgTlRERkUlWByJBQUEICgpyRFuIXEKtESxbrvrwITBzJrBmjfSFoqK0E1Jr1XJsg4mIyhGHTla9du0a7ty5g2vXrkGtVuPcuXMAgIYNG6JKlSqOvDWRReKTMxC7O8Vg59lghQ/m9Q03TOCVkABER2s3rCupShXtktxXX2UvCBGRlWSCIAiOuvjw4cOxdetW0fFDhw6ha9euZs/PycmBQqGASqVCQECAA1pIFVl8cgZitiWh5C+ALpSIG9IGUQ2rAXPnAsuXazetK0HVvhNOzH0X/k0aMfEXEdHfrPn8dmggUloMRMhR1BoBnZceNOgJKU4GoMuDa9h8aA1kKSni8318sPqZUXg/vBcEmXbOt2RPChFRBWTN57db5REhcpaTaXeMBiGV1EUYl7gD4xJ2QCaIe0HutWyLge1fw+VAw7kgmao8xGxL4r4sRERWYCBCFVLWfekgpMmtK1i+ZyUi/koVF3p5QRMbiz7qNrhxv1BUrOtanLnzPPx9PNGhfnUO1RARmeFWCc2InKWmv+EOs3KNGjHHv8C3WydJByGtWwNnzuDEC6Mlg5Di7j0qxOANJ9B56UHEJ0vkGCEiIj0GIlQhtQsLRLDCBzIA9bOv44v/TceMI1vhrS4yqCdUqgTMmwecOAFERBjtSZGiG6phMEJEZByHZsgiFufaKCPkHjLM69MUJ6bEYsaRrfApKhDVyarTEJnvrUXzvt31z1qyJ8UUAdpJr7G7U9AjXFmmf15ERI7CQITMsjjXRlmSloaoCSMQdeSIqEgt88BH7QZiZefBKEjMQ3DKQf2z6npSMlV5omW/UgQAGao8nEy7g8gG3G+GiKgkDs2QSbpcGyVXmFg67KDWCEhMzcY3524gMTUbao2LV4sLAvDRR0DLloBEEHK5WgheGLwUS7sOR0ElTwDaZx2zLQmrD1zEd7/exKAn6wD4J9+IJawZ0iEiqkjYI0JGqTUCYnenSP7lb8mwg9v1pFy/rs1++sMPksU7Ip/HvMjByPM0HH7RPf/KA3/qj1X11QYp93JNT1zVsWZIh4ioImGPCBllKtcGYDjsUFJpe1LsShCAjz8GIiKkg5C6dfHbJ7sw46lRoiDEGFVuIe7lFmLi0w1RtbKn0XoyaIOvdmGBNjaeiKh8YyBCRlk6nFCynrmeFEDbk+KUYZq//gKef167T4xKJS5/9VXg119xKeJJqy6r6xH6/PR1LHq+BWQQD9Xovp/XN5wTVYmIjGAgQkZZOpxQsl5pelLs6osvgObNgW++EZeFhAB79wLr1wMBATYNneieo5qfF+KGtIFSYXgNpcKHWVaJiMzgHBEyytwKERm0H7Ylhx1s7Umxm+xsYOxYYMcO6fIhQ4D33gOqVdMfsnY1THFZ9/PQv1Ut9AhXlqslzkREzsAeETJK7iHDvL7hAKwbdrC1J8Uudu/WzgWRCkJq1AC++gr45BODIAQw/azm6J5D7iFDZIPq6N+qFiIbML07EZElGIiQSVERwVYPOxTPWirFIRM4VSpgxAigXz8gM1NcPnAg8Ntv2vkiRhh7VmM4EZWIqPRkgiC4OLGDcdZsI0yOZW1mVd2qGQAGQx26M+w6d+LAAWDkSCA9XVxWrRqwZg3w8suAzLIeiuLPeuV2LlYduAjACc9BRFROWPP5zUCEHMbheUQePACmTwfi4qTLn31WOxk1JKRUt3G7fChERG6OgQi5jZI9KW3rVsOZq3dLP6Hzp5+A4cOBy5dFRYV+VXDt7f+g3vTxkMvtM/pY3vbaISJyJAYi5Jbs0rPw6BEwezawcqU2UVkJx+q2xPTek3BDUZO9FkRELsJAhNyObs5IyX9sVs21OHlSm5js999FRbme3ljcdQS2tX4WgszD+msTEZHdWPP5zVUz5HClzrRaUKDtBenYUTII+bVOczw7/D180uY5fRBi8bWJiMilmNCMHM6aTKuRDaobFv7yCzBsGPDrr+ITvb1xZfIsDFC3hsZDbv21iYjI5dgjQg5nU6bVoiJg4ULgySelg5C2bYGkJPzy0qtGgxBb2kBERM7FQIQczupMqxcuaIdhZs8GCgsNK1WqBMTGAomJQHi4a7O4EhFRqTEQIYezONNqHQWwfDnQujVw6pS4YosW2gmrc+cCnp7WXZvZT4mI3BIDEXI4S/asWdLKF/Lu3YBp04D8fMNKHh7ArFna4KR1a/1hXW6P3hFKCCauLbUfDhERuQdOViWn0O3jIsoj4u+F9Y9Oo/n//QfIzRWf2KQJsHUr0L69wWGpnCQymWFqESXziBARuT0GIuQ0URHB6BGu1GcorX3/NtrEToPsxwPiyjIZMGmSdsJq5coGRcZykuhW6I7qVA/PhCuZ/ZSIqAxgIEJOJfeQIbJ+ILBlizbQyMkRVwoL05Y/9ZSoyFROEkA7HLM3ORNv9eFwDBFRWcA5IuRcGRlAv37a3XIlgpDMl6OhPveLZBACWJeThIiI3B8DEXIOQQC2bwciIoDvvhMV3/QPwtAX30GHOi+g8wcnEZ+cIXkZm3KSEBGR2+LQjAtUuJ1cb90C3ngD+PJLyeIvI57GO0+PRo5PFQBApioPMduSJPeIKW3ekAr3sycicnMMRJzMLjvQliVffw28/jqQlSUquuNXFTN6jcP+Rh0MjuuW4sbuTkGPcKVBoKDLG5KpypOcJyKDdrWMVN6QCvezJyIqAzg040S61R4l5zjoegCMDUeUFWqNgMTUbHxz7gZOJqVCM3QY8PzzkkHI7Wf74+mRH4iCEB1jcz0syUkilTekvP/siYjKKgYiTlLqHWjdXHxyBjovPYiX1x/HzkUbEdqlPTy2fSKuGBgIbN+OYws/wF1fhdnrSs310OUkUSoMh1+UCh/J4Zzy/rMnIirLODTjJKXagdYFrJlLoett8M3PxaJDm/DKL/HSF+3XD1i3DlAqUTM126J2GJvrUTIniak2lrWfPRFRRcJAxEnK0moPa+ZS6Hob2l/7Fcv2rkao6i/R9R54+8I37gN4DI/WJipD6eZ66Mg9ZBYFDmXpZ09EVNFwaMZJysousdbOpTidch2jd76H7Z+9JRmEHK3XGj1GfoATT/XVByGA7XM9bFFWfvZERBURAxEnKQu7xFo9l+L4cTR/ritGnvlWVP+hpw/e6jUWw158BxkBNewy18NWZeFnT0RUUXFoxkl0PQAx25IgAww+7N1ll1hL51Kc+j0DHT5+D1i2DFU0GlG9E6ERmPbsJKRXVeqP2WOuh63Kws+eiKiiYiDiRMZ2oHWXXWItmSPRPPMSwvtPBS79ISrLq+SF/z4Vjc1P9IUg03a22XOuR2m4+8+eiKiiclggcuXKFSxYsAAHDx5EZmYmQkJCMGTIELz99tvw8vJy1G3dnjN6AGxlao5EJXURxiZ+jnGJO+CpUYvKzwU3xtQ+k5FaPVR/zN16G9z5Z09EVFE5LBD5/fffodFosG7dOjRs2BDJyckYPXo0Hj58iHfffddRty0THNUDUNr05cZWsjS6dRUr9qxAi79SxSd5egLz5+OvZ4ci9/uLgJ16G+yVil3qOlyiS0TkPmSCIDgti9OyZcsQFxeHy5cvW1Q/JycHCoUCKpUKAQEBDm5d2Wav9OW6VTMAINOoMfrULkz5aRu81UXiyq1aAVu3Ai1bArBf8GDPZ2FKdyIi57Pm89upgcjs2bMRHx+P06dPS5bn5+cjPz9f/31OTg5CQ0MZiJihCx5KvkhdCGDtCpT45Axs3LwPM7cvQdubv4sryOXAW28Bs2cDdh5mM/YsOqM61cMz4UqzQY69fyZERGQ5awIRpy3fTU1Nxfvvv48xY8YYrbN48WIoFAr9V2hoqNG6pGX39OUaDaIOfYnP42Kkg5BmzYDEROCdd+wehJh6Fp2Nx67g5fXH0XnpQaP7wzClOxFR2WF1IDJ//nzIZDKTXyV7PG7evImoqCi88MILePXVV41ee9asWVCpVPqv9PR065+ogrEmfblZV64AzzwDTJgA2aNHhmUyGTBtGpCUBDz5ZKnabIy5ZynO1GZ1dv2ZEBGRQ1k9WXXcuHEYNGiQyTr16tXT//fNmzfRrVs3REZG4qOPPjJ5nre3N7y9va1tUoVml/TlggBs3AhMngw8eCAub9BAOxekUycbW2kZa1KsC9AOs8TuTkGPcKXBMA1TuhMRlR1WByJBQUEICgqyqO6NGzfQrVs3tG3bFps3b4aHBxO52lup05ffvAm8+irw/ffS5WPHAkuXAn5+NrbQctamWDe2WR1TuhMRlR0Oiwxu3ryJrl27IjQ0FO+++y5u3bqFzMxMZGZmOuqWFZLN6csFAdi2DWjeXDoIqVMHOHAAWLPGKUEIYP5ZjCnZs8GU7kREZYfDApF9+/bh0qVLOHjwIGrXro3g4GD9F9mPTZvHZWUB//43MHQocO+e+KKjRgHnzwNPP+2IJhtl6llMKdmz4cwN9YiIqHQcFogMHz4cgiBIfpF9WbV53JdfantBdu0SX0ipBHbvBjZsAFy0XNrYs0gx1bPhrA31iIiodJyaR8RaFTmhmS3JwUyec+cOMG4c8Nln0ie/8grw/vtAoHsMV+ieZX9KJjYdu2J0szpzQYW9kqwREZHl3DahmbUqaiBi94yge/YAo0cDGRJ5N4KCgLVrtUM1booZUomIyhYGImWYXTOC5uRol+Ru2iRdPmAAsG4dULOmja11HvZsEBGVHdZ8fjts0zuynrmMoMbyZkj68Udg5Ejg2jVxWdWq2tUwr7yiTVRWBjhqo0AiInItJvZwI3bJCPrwoXYuyDPPSAYhiY2fxKEvfwQGDy4zQQgREZVf7BFxI6XOCHrsGDB8OHDpkqjogVdl/KfbKOx4vBewPwNxj2VwfgUREbkcAxE3YnNG0Lw8YM4cYPlybaKyEhLrtMCbz07CdcVjAKwc4jGB8zaIiKi0GIi4EV1G0ExVntEdaKtW9oRGEKDWCNoP/VOngOho4MIFUd1HlbyxtEs0trZ9DoLsn1E4Y6nRrcGVLEREZA+cI2Jnao2AxNRsfHPuBhJTs63aat6SzKL3HhVi8IYT6LrwB1waMxmIjJQMQpJCmuDZEe9hyxP9DIKQ4mzd9E23sqfkfBZTO+ISERFJYY+IHdmjl0CXEbTkdYprmpWG5XtWomHWZXGhlxeuTpqJ/xPaQuMhN3kvWzZ9s+vKHiIiqvDYI2In9uwliIoIxs8zuuN/o9qjamVP/XG5Ro03Ej/Ht1sno7lUENKmDXDmDGovno/Hqvk5ZNM3u6zsISIi+hsDETsw10sAaHsJrB2m8fCQ4d6jQgBA/ezr+HLbdEw/+jG8NEUGdTWVKgHz5wPHjwMREVZv+mbNcFKpV/YQEREVw6EZO7Cml8CayaFZ9/MgEzQYefpbvHn0Y/gUFYjq/BFUBwdnLUOr/k+jnbwSdIMxxoZ4lCWGiqwdTrJ5ZQ8REZEEBiJ24KhegtC7mdj+2Vton54sKlPLPLCu/UCs6jQYBVmewPrjogAiKiIYPcKVRpfYGksnrxtOkkonb25ljwzaYMeWYR8iIqp4ODRjB3bvJRAEYN06tO7bRTIIuVwtBC8MXor/dhmOgkr/zCGRmo+iS43ev1UtRDaobjAcY8twkrXDPkRERKYwELEDXS+BXSaHpqcDUVHAmDGQPXwoKt7Uth+eHfEekmo1E5VZMx+lNJNOdcM+SoVhYKVU+Fi3KR8REVV4HJqxA10vQcy2JMgAg14Gi3sJBAH4+GNg4kRApRIV36ymxJSoCThep6XJtlg6H6W0w0nmhn2IiIgswR4ROylVL0FmJjBggHafGIkgBK+9hsfS/sDEBa9hWGRdi9pjLtCwx3CSsWEfIiIiS7FHxI5s6iX4/HPgjTeA7GxxWa1awIYNQFQU5AAiFdrDHydeNdsWc4EGJ50SEZE7YCBSCsY2fbNoiW52NjB2LLBjh3T5kCHAe+8B1aoZHLZXAGGX4SQiIqJSYiBioZJBx92HBViwx8Z07rt3A6NHA3/9JS6rWRNYt047VCPBngGEpblGiIiIHEUmCBL7xruJnJwcKBQKqFQqBAQEuKwdUkm/pOg++o3OCVGpgEmTgC1bpC/wf/8HfPghUKOGTW2ydfdbYz07REREtrDm85uBiBnGkn4Zoxsa+XlGd8MP8337gFGjgOvXxSdVq6YNQF56CZBZHgAwgCAiIndkzec3h2ZMMJX0yxjR8tkHD4Dp04G4OOkTnnsO+OgjINj6YRCL56MQERG5KQYiJphL+mVK1v084OhRYMQI4LLETrn+/sCqVdpyK3pBiIiIyhMGIibYuoOsd2E+nnzvP8CWddpEZSU9/TSwaRNQp04pW0hERFS2MRAxwZYdZFvf/AOrvl+FkNvp4kJfX2DZMmDMGMCDueSIiIgqZCBi6SRPczk7ivMqKsTEhM8w5viXkAsacYVOnbSrZRo2tMszEBERlQcVLhCxZtmrqZwdxYX/dRnL96xAs1tXxIXe3sDChdplu3K5vR6DiIioXKhQ4wO6pbglJ6BmqvIQsy0J8ckZonOM7SETrPBB3IstcLTgGL7bNkU6CHnySeDsWWDqVAYhREREEipMj4ippbgCtPk/YnenoEe4UjRMI7mHzKNMyEf8Gzh9WnxBT09g3jxgxgygUoX5ERMREVmtwnxKmluKK8r/UYI+Z4daDaxcCcyeDeTniy/UsiWwdSvQqpX9Gm9nTIRGRETuosIEIpYuxTVZ79IlYPhw4NgxcZmHBzBrFjB3LuDlZVsjncCeqeGJiIhKq8LMEbF0Ke7t+/lQa0oM4Gg02hTsjz8uHYQ0aQIkJAD/+Y8oCFFrBCSmZuObczeQmJotvraFdezBljkyREREjlRhekQsXYq7YM8FbPg57Z8egmvXgJEjgR9/FFeWyYDJk7UBSOXKomJLeh+c1UNRmjkyREREjlJhekR0S3GBf3bJNSZTlYeYT87g/IIVQESEdBBSvz5w5AiwfLnRIMRc74MzeyismSNDRETkLBWmRwT4ZyluyR6IkoIe3MGS+PfRIvWUdIUxY7QZUqtUkSy2pPdh/re/AZA5rYfCLnNkiIiI7KzC9IjoREUE4+cZ3TGnTzNxoSCgX8ph7N/4Bp6WCkJq1wb27dPupGskCAEs633IzMlHZo7zeigsnSNjS1p7IiIiW1WoHhEduYcMQf7eBscCc1X4zw8f4NmLCdInDR+u3S1XoTB7fXv2KtjrWubmyMgAKBXapbxERETOUuF6RHSK/+Xf62IC9m18QzIIKQiqCXz7LbB5s0VBSMlr27OdpWFqjozu+3l9wzlRlYiInMqhgUi/fv1Qp04d+Pj4IDg4GEOHDsXNmzcdeUuLtQsLRGOvQqz8bjnW7VqEoFyVqM6+lt0gT/kN6NvX6msHK3yMToqVAVAGeEMZYLpOsJ17KIylq1cqfBA3pA3ziBARkdPJBEFwTNIKACtXrkRkZCSCg4Nx48YNTJs2DQCQkGBk+KOEnJwcKBQKqFQqBAQE2Ldx8fHIix4Bn6xMUdGdygGY2yMGzy2caPOHs25FDGC4WZ4u8Igb0gYAzNZxRHDAzKpERORI1nx+OzQQKenbb7/FgAEDkJ+fD09PT7P1HRKI3L+v3YRu/XrJ4v0N22PlC9Mw4ZVOpQ4C3CmPCBERkbO4ZSBy584dxMTE4MaNG/j5558l6+Tn5yO/2P4tOTk5CA0NtV8gUlio3Qvm999FRUX+Afhl+jsoGDQY7epXt1sPgSW9D+yhICKi8sSaQMThq2ZmzJiBNWvWIDc3Fx06dMB3331ntO7ixYsRGxvruMZ4egKjRgFvvml4vGdPVNq4EW1r17b7LfWb5ZWyDhERUXlk9WTV+fPnQyaTmfw6ffq0vv6bb76Js2fPYt++fZDL5Rg2bBiMdcLMmjULKpVK/5Wenm77kxkzeTLQqZP2v6tUAdatA+LjtTlCiIiIyKmsHpq5ffs2bt++bbJOvXr14OMjXnZ6/fp1hIaGIiEhAZGRkWbv5bDJqpcuARMmAB98AISF2e+6RERE5NihmaCgIAQFBdnUMF3MU3weiEs0bAjs3evaNhAREZHj5oicPHkSJ0+eROfOnVGtWjVcvnwZc+fORYMGDSzqDSEiIqLyz2EJzSpXroyvvvoKTz/9NJo0aYKRI0ciIiICR44cgbe3t/kLEBERUbnnsB6RFi1a4ODBg466PBEREZUDFXavGSIiInI9BiJERETkMgxEiIiIyGUYiBAREZHLMBAhIiIil2EgQkRERC7DQISIiIhchoEIERERuYzDEpqVdWqNgJNpd5B1Pw81/X3QLiwQcg+Zq5tFRERUrjAQkRCfnIHY3SnIUOXpjwUrfDCvbziiIoJd2DIiIqLyhUMzJcQnZyBmW5JBEAIAmao8xGxLQnxyhotaRkREVP4wEClGrREQuzsFgkSZ7ljs7hSoNVI1iIiIyFoMRIo5mXZH1BNSnAAgQ5WHk2l3nNcoIiKicoyBSDFZ940HIbbUIyIiItMYiBRT09/HrvWIiIjINAYixbQLC0SwwgfGFunKoF090y4s0JnNIiIiKrcYiBQj95BhXt9wABAFI7rv5/UNZz4RIiIiO2EgUkJURDDihrSBUmE4/KJU+CBuSBvmESEiIrIjJjSTEBURjB7hSmZWJSIicjAGIkbIPWSIbFDd1c0gIiIq1zg0Q0RERC7DQISIiIhchoEIERERuQwDESIiInIZBiJERETkMgxEiIiIyGUYiBAREZHLMBAhIiIil2EgQkRERC7j1plVBUEAAOTk5Li4JURERGQp3ee27nPcFLcORO7fvw8ACA0NdXFLiIiIyFr379+HQqEwWUcmWBKuuIhGo8HNmzfh7+8Pmcy+G87l5OQgNDQU6enpCAgIsOu13QGfr+wr789Y3p8PKP/PyOcr+xz1jIIg4P79+wgJCYGHh+lZIG7dI+Lh4YHatWs79B4BAQHl9h8YwOcrD8r7M5b35wPK/zPy+co+RzyjuZ4QHU5WJSIiIpdhIEJEREQuU2EDEW9vb8ybNw/e3t6ubopD8PnKvvL+jOX9+YDy/4x8vrLPHZ7RrSerEhERUflWYXtEiIiIyPUYiBAREZHLMBAhIiIil2EgQkRERC5TbgORhQsXomPHjvD19UXVqlUtOkcQBMyfPx8hISGoXLkyunbtit9++82gTn5+PsaPH4+goCD4+fmhX79+uH79ugOewLS7d+9i6NChUCgUUCgUGDp0KO7du2fyHJlMJvm1bNkyfZ2uXbuKygcNGuTgp5FmyzMOHz5c1P4OHToY1Cmr77CwsBAzZsxAixYt4Ofnh5CQEAwbNgw3b940qOfKd/jhhx8iLCwMPj4+aNu2LX766SeT9Y8cOYK2bdvCx8cH9evXx9q1a0V1du7cifDwcHh7eyM8PBy7du1yVPPNsub5vvrqK/To0QM1atRAQEAAIiMj8cMPPxjU2bJli+TvZF5enqMfRZI1z3f48GHJtv/+++8G9dzp/QHWPaPU/09kMhmaN2+ur+NO7/Do0aPo27cvQkJCIJPJ8PXXX5s9xy1+B4Vyau7cucKKFSuEKVOmCAqFwqJzlixZIvj7+ws7d+4Uzp8/L7z00ktCcHCwkJOTo68zZswYoVatWsL+/fuFpKQkoVu3bsLjjz8uFBUVOehJpEVFRQkRERFCQkKCkJCQIERERAjPPfecyXMyMjIMvjZt2iTIZDIhNTVVX6dLly7C6NGjDerdu3fP0Y8jyZZnjI6OFqKiogzan52dbVCnrL7De/fuCc8884ywY8cO4ffffxcSExOF9u3bC23btjWo56p3uH37dsHT01NYv369kJKSIkycOFHw8/MTrl69Kln/8uXLgq+vrzBx4kQhJSVFWL9+veDp6Sl8+eWX+joJCQmCXC4XFi1aJFy4cEFYtGiRUKlSJeH48eMOf56SrH2+iRMnCkuXLhVOnjwpXLx4UZg1a5bg6ekpJCUl6ets3rxZCAgIEP1uuoK1z3fo0CEBgPDHH38YtL3475E7vT9BsP4Z7927Z/Bs6enpQmBgoDBv3jx9HXd6h3v37hXefvttYefOnQIAYdeuXSbru8vvYLkNRHQ2b95sUSCi0WgEpVIpLFmyRH8sLy9PUCgUwtq1awVB0P6j9PT0FLZv366vc+PGDcHDw0OIj4+3e9uNSUlJEQAY/ENITEwUAAi///67xdfp37+/0L17d4NjXbp0ESZOnGivptrM1meMjo4W+vfvb7S8vL3DkydPCgAM/kfqqnfYrl07YcyYMQbHmjZtKsycOVOy/vTp04WmTZsaHHv99deFDh066L9/8cUXhaioKIM6vXr1EgYNGmSnVlvO2ueTEh4eLsTGxuq/t/T/T85g7fPpApG7d+8avaY7vT9BKP073LVrlyCTyYQrV67oj7nTOyzOkkDEXX4Hy+3QjLXS0tKQmZmJnj176o95e3ujS5cuSEhIAACcOXMGhYWFBnVCQkIQERGhr+MMiYmJUCgUaN++vf5Yhw4doFAoLG7HX3/9hT179mDUqFGisv/9738ICgpC8+bNMW3aNP0uyM5Ummc8fPgwatasicaNG2P06NHIysrSl5WndwgAKpUKMplMNPzo7HdYUFCAM2fOGPxcAaBnz55GnycxMVFUv1evXjh9+jQKCwtN1nHmuwJse76SNBoN7t+/j8DAQIPjDx48QN26dVG7dm0899xzOHv2rN3abanSPF/r1q0RHByMp59+GocOHTIoc5f3B9jnHW7cuBHPPPMM6tata3DcHd6hLdzld9CtN71zpszMTADAY489ZnD8sccew9WrV/V1vLy8UK1aNVEd3fnOkJmZiZo1a4qO16xZ0+J2bN26Ff7+/hg4cKDB8cGDByMsLAxKpRLJycmYNWsWfvnlF+zfv98ubbeUrc/Yu3dvvPDCC6hbty7S0tIwZ84cdO/eHWfOnIG3t3e5eod5eXmYOXMmXnnlFYPNqlzxDm/fvg21Wi35+2PseTIzMyXrFxUV4fbt2wgODjZax5nvCrDt+Upavnw5Hj58iBdffFF/rGnTptiyZQtatGiBnJwcrF69Gp06dcIvv/yCRo0a2fUZTLHl+YKDg/HRRx+hbdu2yM/PxyeffIKnn34ahw8fxlNPPQXA+Dt29vsDSv8OMzIy8P333+PTTz81OO4u79AW7vI7WKYCkfnz5yM2NtZknVOnTuGJJ56w+R4ymczge0EQRMdKsqSOJSx9PkDcTmvbsWnTJgwePBg+Pj4Gx0ePHq3/74iICDRq1AhPPPEEkpKS0KZNG4uubYqjn/Gll17S/3dERASeeOIJ1K1bF3v27BEFXdZc11LOeoeFhYUYNGgQNBoNPvzwQ4MyR79DU6z9/ZGqX/K4Lb+TjmJrWz777DPMnz8f33zzjUEA2qFDB4PJ1J06dUKbNm3w/vvv47333rNfwy1kzfM1adIETZo00X8fGRmJ9PR0vPvuu/pAxNprOoOt7dmyZQuqVq2KAQMGGBx3t3doLXf4HSxTgci4cePMzv6vV6+eTddWKpUAtBFicHCw/nhWVpY+GlQqlSgoKMDdu3cN/qLOyspCx44dbbpvcZY+36+//oq//vpLVHbr1i1R5Crlp59+wh9//IEdO3aYrdumTRt4enrizz//tMuHmLOeUSc4OBh169bFn3/+CaB8vMPCwkK8+OKLSEtLw8GDB81u3W3vdyglKCgIcrlc9FdS8d+fkpRKpWT9SpUqoXr16ibrWPNvwB5seT6dHTt2YNSoUfjiiy/wzDPPmKzr4eGBJ598Uv/v1VlK83zFdejQAdu2bdN/7y7vDyjdMwqCgE2bNmHo0KHw8vIyWddV79AWbvM7aLfZJm7K2smqS5cu1R/Lz8+XnKy6Y8cOfZ2bN2+6bKLjiRMn9MeOHz9u8UTH6Oho0UoLY86fPy8AEI4cOWJze21R2mfUuX37tuDt7S1s3bpVEISy/w4LCgqEAQMGCM2bNxeysrIsupez3mG7du2EmJgYg2PNmjUzOVm1WbNmBsfGjBkjmijXu3dvgzpRUVEum6xqzfMJgiB8+umngo+Pj9lJgzoajUZ44oknhBEjRpSmqTax5flK+ve//y1069ZN/707vT9BsP0ZdRNzz58/b/YernyHxcHCyaru8DtYbgORq1evCmfPnhViY2OFKlWqCGfPnhXOnj0r3L9/X1+nSZMmwldffaX/fsmSJYJCoRC++uor4fz588LLL78suXy3du3awoEDB4SkpCShe/fuLlv62bJlSyExMVFITEwUWrRoIVr6WfL5BEEQVCqV4OvrK8TFxYmueenSJSE2NlY4deqUkJaWJuzZs0do2rSp0Lp1a6c/nyBY/4z3798Xpk6dKiQkJAhpaWnCoUOHhMjISKFWrVrl4h0WFhYK/fr1E2rXri2cO3fOYKlgfn6+IAiufYe6pZEbN24UUlJShEmTJgl+fn76FQYzZ84Uhg4dqq+vWzo4efJkISUlRdi4caNo6eCxY8cEuVwuLFmyRLhw4YKwZMkSly/ftfT5Pv30U6FSpUrCBx98YHQp9fz584X4+HghNTVVOHv2rDBixAihUqVKBgGquz7fypUrhV27dgkXL14UkpOThZkzZwoAhJ07d+rruNP7EwTrn1FnyJAhQvv27SWv6U7v8P79+/rPOgDCihUrhLNnz+pX1bnr72C5DUSio6MFAKKvQ4cO6esAEDZv3qz/XqPRCPPmzROUSqXg7e0tPPXUU6II+NGjR8K4ceOEwMBAoXLlysJzzz0nXLt2zUlP9Y/s7Gxh8ODBgr+/v+Dv7y8MHjxYtIyu5PMJgiCsW7dOqFy5smReiWvXrglPPfWUEBgYKHh5eQkNGjQQJkyYIMrD4SzWPmNubq7Qs2dPoUaNGoKnp6dQp04dITo6WvR+yuo7TEtLk/w3Xfzftavf4QcffCDUrVtX8PLyEtq0aWPQCxMdHS106dLFoP7hw4eF1q1bC15eXkK9evUkA+QvvvhCaNKkieDp6Sk0bdrU4IPO2ax5vi5duki+q+joaH2dSZMmCXXq1BG8vLyEGjVqCD179hQSEhKc+ESGrHm+pUuXCg0aNBB8fHyEatWqCZ07dxb27NkjuqY7vT9BsP7f6L1794TKlSsLH330keT13Okd6npujP2bc9ffQZkg/D0zhYiIiMjJmEeEiIiIXIaBCBEREbkMAxEiIiJyGQYiRERE5DIMRIiIiMhlGIgQERGRyzAQISIiIpdhIEJEREQuw0CEiIiIXIaBCBEREbkMAxEiIiJyGQYiRERE5DL/D/unfdzP3T2HAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: 3.0109417\n",
      "b: 0.008163533\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "\n",
    "sess = tf.compat.v1.Session()\n",
    "\n",
    "x_data = np.linspace(-1, 1, 100)\n",
    "y_data = 3 * x_data + np.random.randn(*x_data.shape) * 0.3 \n",
    "\n",
    "x = tf.compat.v1.placeholder(dtype=tf.float32)\n",
    "y = tf.compat.v1.placeholder(dtype=tf.float32)\n",
    "\n",
    "w = tf.Variable(0.0, dtype=tf.float32, name=\"w\")\n",
    "b = tf.Variable(0.0, dtype=tf.float32, name=\"b\")\n",
    "\n",
    "pred = w * x + b\n",
    "\n",
    "loss_function = tf.reduce_mean(tf.square(y - pred))\n",
    "\n",
    "optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss_function)\n",
    "\n",
    "sess.run(tf.compat.v1.global_variables_initializer())\n",
    "\n",
    "for step in range(1000):\n",
    "    sess.run(optimizer, feed_dict={x: x_data, y: y_data})\n",
    "\n",
    "plt.scatter(x_data, y_data, label='Real data')\n",
    "\n",
    "plt.plot(x_data,\n",
    "         sess.run(pred, feed_dict={x: x_data}),\n",
    "         label='Fitted line',  \n",
    "         color='r',  \n",
    "         linewidth=3) \n",
    "\n",
    "plt.legend(loc=2) \n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"w:\", sess.run(w))\n",
    "print(\"b:\", sess.run(b)) \n",
    "\n",
    "sess.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_new: [0.42716402]\n",
      "目标值: 1.854328\n",
      "预测值: 1.251196\n",
      "预测值与真实值的绝对误差: [0.60313237]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "\n",
    "sess = tf.compat.v1.Session()\n",
    "x = tf.compat.v1.placeholder(dtype=tf.float32)\n",
    "y = tf.compat.v1.placeholder(dtype=tf.float32)\n",
    "\n",
    "w = tf.Variable(0.0, dtype=tf.float32, name=\"w\")\n",
    "b = tf.Variable(0.0, dtype=tf.float32, name=\"b\")\n",
    "\n",
    "pred = w * x + b\n",
    "\n",
    "loss_function = tf.reduce_mean(tf.square(y - pred))\n",
    "\n",
    "optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss_function)\n",
    "\n",
    "sess.run(tf.compat.v1.global_variables_initializer())\n",
    "\n",
    "x_data = np.linspace(-1, 1, 100)\n",
    "y_data = 3 * x_data + np.random.randn(*x_data.shape) * 0.3\n",
    "for step in range(1000):\n",
    "    sess.run(optimizer, feed_dict={x: x_data, y: y_data})\n",
    "\n",
    "x_new = np.random.normal(loc=0, scale=1, size=(1,)) \n",
    "print(\"x_new:\", x_new)\n",
    "\n",
    "print(\"目标值: %f\" % target)\n",
    "\n",
    "predict = sess.run(pred, feed_dict={x: x_new})\n",
    "print(\"预测值: %f\" % predict)\n",
    "\n",
    "print(\"预测值与真实值的绝对误差:\", abs(target - predict))\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checkpoints: None\n",
      "No checkpoint found, initializing variables.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "sess = tf.compat.v1.Session()\n",
    "\n",
    "ckpt_dir = './model_checkpoints'\n",
    "\n",
    "saver = tf.compat.v1.train.Saver()\n",
    "\n",
    "ckpt = tf.compat.v1.train.get_checkpoint_state(ckpt_dir)\n",
    "print(\"Checkpoints:\", ckpt)\n",
    "\n",
    "if ckpt and ckpt.model_checkpoint_path:\n",
    "    try:\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        print(\"Model restored from checkpoint:\", ckpt.model_checkpoint_path)\n",
    "    except Exception as e:\n",
    "        print(\"Error restoring model:\", e)\n",
    "else:\n",
    "    print(\"No checkpoint found, initializing variables.\")\n",
    "    sess.run(tf.compat.v1.global_variables_initializer())\n",
    "\n",
    "sess.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch:01 Step:010 loss=0.471074015\n",
      "Train Epoch:01 Step:020 loss=0.027650634\n",
      "Train Epoch:01 Step:030 loss=0.081844635\n",
      "Train Epoch:01 Step:040 loss=0.007880119\n",
      "Train Epoch:01 Step:050 loss=0.264854133\n",
      "Train Epoch:01 Step:060 loss=0.012589065\n",
      "Train Epoch:01 Step:070 loss=0.028636044\n",
      "Train Epoch:01 Step:080 loss=0.025078457\n",
      "Train Epoch:01 Step:090 loss=0.532225490\n",
      "Train Epoch:01 Step:100 loss=0.015161546\n",
      "Model_of_Epoch_1 saved!\n",
      "Train Epoch:02 Step:110 loss=0.364564002\n",
      "Train Epoch:02 Step:120 loss=0.078873605\n",
      "Train Epoch:02 Step:130 loss=0.019722352\n",
      "Train Epoch:02 Step:140 loss=0.069795482\n",
      "Train Epoch:02 Step:150 loss=0.102184385\n",
      "Train Epoch:02 Step:160 loss=0.006863941\n",
      "Train Epoch:02 Step:170 loss=0.000017388\n",
      "Train Epoch:02 Step:180 loss=0.088108435\n",
      "Train Epoch:02 Step:190 loss=0.392657787\n",
      "Train Epoch:02 Step:200 loss=0.038965948\n",
      "Model_of_Epoch_2 saved!\n",
      "Train Epoch:03 Step:210 loss=0.408733755\n",
      "Train Epoch:03 Step:220 loss=0.080338098\n",
      "Train Epoch:03 Step:230 loss=0.018508106\n",
      "Train Epoch:03 Step:240 loss=0.072644494\n",
      "Train Epoch:03 Step:250 loss=0.098424174\n",
      "Train Epoch:03 Step:260 loss=0.007883325\n",
      "Train Epoch:03 Step:270 loss=0.000089297\n",
      "Train Epoch:03 Step:280 loss=0.090629347\n",
      "Train Epoch:03 Step:290 loss=0.388740182\n",
      "Train Epoch:03 Step:300 loss=0.039863855\n",
      "Model_of_Epoch_3 saved!\n",
      "Train Epoch:04 Step:310 loss=0.410118341\n",
      "Train Epoch:04 Step:320 loss=0.080382906\n",
      "Train Epoch:04 Step:330 loss=0.018471733\n",
      "Train Epoch:04 Step:340 loss=0.072732136\n",
      "Train Epoch:04 Step:350 loss=0.098310761\n",
      "Train Epoch:04 Step:360 loss=0.007915482\n",
      "Train Epoch:04 Step:370 loss=0.000092362\n",
      "Train Epoch:04 Step:380 loss=0.090706736\n",
      "Train Epoch:04 Step:390 loss=0.388620973\n",
      "Train Epoch:04 Step:400 loss=0.039891373\n",
      "Model_of_Epoch_4 saved!\n",
      "Train Epoch:05 Step:410 loss=0.410160184\n",
      "Train Epoch:05 Step:420 loss=0.080384322\n",
      "Train Epoch:05 Step:430 loss=0.018470598\n",
      "Train Epoch:05 Step:440 loss=0.072734773\n",
      "Train Epoch:05 Step:450 loss=0.098307341\n",
      "Train Epoch:05 Step:460 loss=0.007916457\n",
      "Train Epoch:05 Step:470 loss=0.000092454\n",
      "Train Epoch:05 Step:480 loss=0.090709038\n",
      "Train Epoch:05 Step:490 loss=0.388617426\n",
      "Train Epoch:05 Step:500 loss=0.039892230\n",
      "Model_of_Epoch_5 saved!\n",
      "Train Epoch:06 Step:510 loss=0.410161704\n",
      "Train Epoch:06 Step:520 loss=0.080384322\n",
      "Train Epoch:06 Step:530 loss=0.018470565\n",
      "Train Epoch:06 Step:540 loss=0.072734870\n",
      "Train Epoch:06 Step:550 loss=0.098307230\n",
      "Train Epoch:06 Step:560 loss=0.007916478\n",
      "Train Epoch:06 Step:570 loss=0.000092456\n",
      "Train Epoch:06 Step:580 loss=0.090709105\n",
      "Train Epoch:06 Step:590 loss=0.388617128\n",
      "Train Epoch:06 Step:600 loss=0.039892327\n",
      "Model_of_Epoch_6 saved!\n",
      "Train Epoch:07 Step:610 loss=0.410161704\n",
      "Train Epoch:07 Step:620 loss=0.080384322\n",
      "Train Epoch:07 Step:630 loss=0.018470565\n",
      "Train Epoch:07 Step:640 loss=0.072734900\n",
      "Train Epoch:07 Step:650 loss=0.098307230\n",
      "Train Epoch:07 Step:660 loss=0.007916478\n",
      "Train Epoch:07 Step:670 loss=0.000092456\n",
      "Train Epoch:07 Step:680 loss=0.090709105\n",
      "Train Epoch:07 Step:690 loss=0.388617128\n",
      "Train Epoch:07 Step:700 loss=0.039892327\n",
      "Model_of_Epoch_7 saved!\n",
      "Train Epoch:08 Step:710 loss=0.410161704\n",
      "Train Epoch:08 Step:720 loss=0.080384322\n",
      "Train Epoch:08 Step:730 loss=0.018470565\n",
      "Train Epoch:08 Step:740 loss=0.072734900\n",
      "Train Epoch:08 Step:750 loss=0.098307230\n",
      "Train Epoch:08 Step:760 loss=0.007916478\n",
      "Train Epoch:08 Step:770 loss=0.000092456\n",
      "Train Epoch:08 Step:780 loss=0.090709105\n",
      "Train Epoch:08 Step:790 loss=0.388617128\n",
      "Train Epoch:08 Step:800 loss=0.039892327\n",
      "Model_of_Epoch_8 saved!\n",
      "Train Epoch:09 Step:810 loss=0.410161704\n",
      "Train Epoch:09 Step:820 loss=0.080384322\n",
      "Train Epoch:09 Step:830 loss=0.018470565\n",
      "Train Epoch:09 Step:840 loss=0.072734900\n",
      "Train Epoch:09 Step:850 loss=0.098307230\n",
      "Train Epoch:09 Step:860 loss=0.007916478\n",
      "Train Epoch:09 Step:870 loss=0.000092456\n",
      "Train Epoch:09 Step:880 loss=0.090709105\n",
      "Train Epoch:09 Step:890 loss=0.388617128\n",
      "Train Epoch:09 Step:900 loss=0.039892327\n",
      "Model_of_Epoch_9 saved!\n",
      "Train Epoch:10 Step:910 loss=0.410161704\n",
      "Train Epoch:10 Step:920 loss=0.080384322\n",
      "Train Epoch:10 Step:930 loss=0.018470565\n",
      "Train Epoch:10 Step:940 loss=0.072734900\n",
      "Train Epoch:10 Step:950 loss=0.098307230\n",
      "Train Epoch:10 Step:960 loss=0.007916478\n",
      "Train Epoch:10 Step:970 loss=0.000092456\n",
      "Train Epoch:10 Step:980 loss=0.090709105\n",
      "Train Epoch:10 Step:990 loss=0.388617128\n",
      "Train Epoch:10 Step:1000 loss=0.039892327\n",
      "Model_of_Epoch_10 saved!\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "with tf.compat.v1.Session() as sess:\n",
    "\n",
    "    sess.run(tf.compat.v1.global_variables_initializer())\n",
    "\n",
    "    step = 0 \n",
    "    loss_list = []  \n",
    "    display_step = 10  \n",
    "    loss_train_list = []  \n",
    "\n",
    "    for epoch in range(train_epochs):\n",
    "        for xs, ys in zip(x_data, y_data):\n",
    "\n",
    "            _, loss = sess.run([optimizer, loss_function], feed_dict={x: xs, y: ys})\n",
    "            loss_list.append(loss)  \n",
    "            step += 1  \n",
    "            if step % display_step == 0:  \n",
    "                print(\"Train Epoch:%02d\" % (epoch + 1), \"Step:%03d\" % (step), \"loss={:.9f}\".format(loss))  # 显示小数点后9位\n",
    "\n",
    "        b_temp = b.eval(session=sess)\n",
    "        w_temp = w.eval(session=sess)\n",
    "\n",
    "        plt.plot(x_data, w_temp * x_data + b_temp)  \n",
    "\n",
    "\n",
    "        loss_train = sess.run(loss_function, feed_dict={x: x_data, y: y_data})\n",
    "        loss_train_list.append(loss_train)\n",
    "\n",
    "        saver.save(sess, save_path=ckpt_dir + f'Model_of_Epoch_{epoch+1}.ckpt')\n",
    "        print(f'Model_of_Epoch_{epoch+1} saved!')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13719ca82e0>]"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure()\n",
    "plt.plot(loss_list, '-r+') \n",
    "\n",
    "plt.figure()\n",
    "plt.plot(loss_train_list, '--go') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8.123153, 4.649746, 1.2280686, 6.580062, 4.0666695, 1.1107394]"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[xx for xx in loss_list if xx>1]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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